Zhi-Hua Zhou's Publications

Zhi-Hua Zhou's Publications

[Selected International Publications][Full List][List by Topic]{DBLP}{GoogleScholar} [Selected Native Publications]

ML/DM Topics: [Multi-Label Learning] [Multi-Instance Learning] [Multi-View Learning] [Semi-Supervised and Active Learning] [Cost-Sensitive and Class-Imbalance Learning] [Metric Learning, Dimensionality Reduction and Feature Selection] [Ensemble Learning] [Structure Learning and Clustering] [Crowdsourcing Learning] [Logic Learning]

Applications: [Image Retrieval] [Web Search and Mining] [Face Recognition] [Computer-Aided Medical Diagnosis] [Bioinformatics] [Software Mining]

Other Topics: [Theoretical Aspects of Evolutionary Computation] [Improving Comprehensibility]

[Miscellaneous]

Note: The same article may appear under different topics

Multi-Label Learning

Y. Zhu, K. M. Ting, and Z.-H. Zhou. Multi-label learning with emerging new labels. IEEE Transactions on Knowledge and Data Engineering, in press. [code]

Y. Zhu, J. Kwok, and Z.-H. Zhou. Multi-label learning with global and local correlation. IEEE Transactions on Knowledge and Data Engineering, in press. (CORR abs/1704.01415) [code]

P. Zhao and Z.-H. Zhou. Label distribution learning by optimal transport. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018.

H.-C. Dong, Y.-F. Li, and Z.-H. Zhou. Learning from semi-supervised weak-label data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018.

C. Liu, P. Zhao, S.-J. Huang, Y. Jiang, and Z.-H. Zhou. Dual set multi-label learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018.

X.-Z. Wu and Z.-H. Zhou. A unified view of multi-label performance measures. In: Proceedings of the 34th International Conference on Machine Learning (ICML'17), Sydney, Australia, 2017, pp.3780-3788. (CORR abs/1609.00288)

M. Xu and Z.-H. Zhou. Incomplete label distribution learning. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.3175-3181.

Z.-H. Zhou and M.-L. Zhang. Multi-label learning. In: C. Sammut, G. I. Webb, eds. Encyclopedia of Machine Learning and Data Mining, Berlin: Springer, 2017, 875-881.

J. Feng and Z.-H. Zhou. DeepMIML network. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017, pp.1884-1890.

Y. Zhu, K. M. Ting, and Z.-H. Zhou. Discover multiple novel labels in multi-instance multi-label learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017, pp.2977-2983. [code]

D.-C. Zhan, J. Tang, and Z.-H. Zhou. Online game props recommendation with real assessments. Complex & Intelligent Systems, 2017, 3(1): 1-15.

Y. Zhu, K. M. Ting, and Z.-H. Zhou. Multi-label learning with emerging new labels. In: Proceedings of the 16th IEEE International Conference on Data Mining (ICDM'16), Barcelona, Spain, 2016, pp.1371-1376. [code]

S.-J. Huang, S. Chen, and Z.-H. Zhou. Multi-label active learning: Query type matters. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.946-952. [code]

N. Li, Y. Jiang, and Z.-H. Zhou. Multi-label selective ensemble. In: Proceedings of the 12th International Workshop on Multiple Classifier Systems (MCS'15), LNCS 9132, Günzburg, Germany, 2015. pp.76-88. [code]

J.-H. Hu, D.-C. Zhan, X. Wu, Y. Jiang, and Z.-H. Zhou. Pairwised specific distance learning from physical linkages. ACM Transactions on Knowledge Discovery from Data, 2015, 9(3): Article 20.

J.-S. Wu, S.-J. Huang, and Z.-H. Zhou. Genome-wide protein function prediction through multi-instance multi-label learning. ACM/IEEE Transactions on Computational Biology and Bioinformatics, 2014, 11(5): 891-902. [code] [data]

S.-J. Huang, W. Gao, and Z.-H. Zhou. Fast multi-instance multi-label learning. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), Quebec City, Canada, 2014, pp.1868-1874. (CORR abs/1310.2049) [code]

C.-T. Nguyen, X. Wang, J. Liu, and Z.-H. Zhou. Labeling complicated objects: Multi-view multi-instance multi-label learning. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), Quebec City, Canada, 2014, pp.2013-2019. [code]

S.-J. Huang, R. Jin, and Z.-H. Zhou. Active learning by querying informative and representative examples. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(10): 1936-1949. [code]

M.-L. Zhang and Z.-H. Zhou. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837.

M. Xu, R. Jin, and Z.-H. Zhou. Speedup matrix completion with side information: Application to multi-label learning. In: Advances in Neural Information Processing Systems 26 (NIPS'13) (Lake Tahoe, NV), C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K. Q. Weinberger, eds. Cambridge, MA: MIT Press, 2013, pp.2301-2309. [supplement][code]

S.-J. Huang and Z.-H. Zhou. Active query driven by uncertainty and diversity for incremental multi-label learning. In: Proceedings of the 13th IEEE International Conference on Data Mining (ICDM'13), Dallas, TX, 2013, pp.1079-1084. [code]

C.-T. Nguyen, D.-C. Zhan, and Z.-H. Zhou. Multi-modal image annotation with multi-instance multi-label LDA. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI'13), Beijing, China, 2013, pp.1558-1564. [code]

S.-J. Yang, Y. Jiang, and Z.-H. Zhou. Multi-instance multi-label learning with weak label. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI'13), Beijing, China, 2013, pp.1862-1868. [code]

M. Xu, Y.-F. Li, and Z.-H. Zhou. Multi-label learning with PRO loss. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI'13), Bellevue, WA, 2013, pp.998-1004. [code]

W. Gao and Z.-H. Zhou. On the consistency of multi-label learning. Artificial Intelligence, 2013, 199-200: 22-44.

X. Geng, C. Yin, and Z.-H. Zhou. Facial age estimation by label distribution learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2401-2412.

X.-S. Xu, Y. Jiang, X. Xue and Z.-H. Zhou. Semi-supervised multi-instance multi-label learning for video annotation task. In: Proceedings of the 20th ACM International Conference on Multimedia (MM'12), Nara, Japan, 2012, pp.737-740. (short paper)

S.-J. Huang, Y. Yu, and Z.-H. Zhou. Multi-label hypothesis reuse. In: Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'12), Beijing, China, 2012, pp.525-533. [code]

S.-J. Huang and Z.-H. Zhou. Multi-label learning by exploiting label correlations locally. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI'12), Toronto, Canada, 2012, pp.949-955. [code]

Y.-F. Li, J.-H. Hu, Y. Jiang, and Z.-H. Zhou. Towards discovering what patterns trigger what labels. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI'12), Toronto, Canada, 2012, pp.1012-1018. [code]

W. Wang and Z.-H. Zhou. Learnability of multi-instance multi-label learning. Chinese Science Bulletin, 2012, 57(19): 2488-2491.

Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, and Y.-F. Li. Multi-instance multi-label learning. Artificial Intelligence, 2012, 176(1): 2291-2320. (CORR abs/0808.3231) [supplement] [code]

Y.-X. Li, S. Ji, S. Kumar, J. Ye, and Z.-H. Zhou. Drosophila gene expression pattern annotation through multi-instance multi-label learning. ACM/IEEE Transactions on Computational Biology and Bioinformatics, 2012, 9(1): 98-112. [code]

X.-S. Xu, X. Xue, and Z.-H. Zhou. Ensemble multi-instance multi-label learning approach for video annotation task. In: Proceedings of the 19th ACM International Conference on Multimedia (MM'11), Scottsdale, AZ, 2011, pp.1153-1156. (short paper)

X.-S. Xu, Y. Jiang, P. Liang, X. Xue, and Z.-H. Zhou. Ensemble approach based on conditional random field for multi-label image and video annotation. In: Proceedings of the 19th ACM International Conference on Multimedia (MM'11), Scottsdale, AZ, 2011, pp.1377-1380. (short paper)

W. Gao and Z.-H. Zhou. On the consistency of multi-label learning. In: Proceedings of the 24th Annual Conference on Learning Theory (COLT'11), Budapest, Hungary, 2011, JMLR: W&CP 19, pp.341-358.

X. Kong, M. K. Ng, and Z.-H. Zhou. Transductive multilabel learning via label set propagation. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(3): 704-719. [code]

Y.-Y. Sun, Y. Zhang, and Z.-H. Zhou. Multi-label learning with weak label. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA, 2010, pp.593-598. [code]

X. Geng, K. Smith-Miles, and Z.-H. Zhou. Facial age estimation by learning from label distribution. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA, 2010, pp.451-456.

Y. Zhang and Z.-H. Zhou. Multi-label dimensionality reduction via dependence maximization. ACM Transactions on Knowledge Discovery from Data, 2010, 4(3): Article 14. [code & data]

Y.-X. Li, S. Ji, J. Ye, S. Kumar, and Z.-H. Zhou. Drosophila gene expression pattern annotation through multi-instance multi-label learning. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1445-1450. [code]

R. Jin, S. Wang, and Z.-H. Zhou. Learning a distance metric from multi-instance multi-label data. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, 2009, pp.896-902.

M.-L. Zhang and Z.-H. Zhou. M³MIML: A maximum margin method for multi-instance multi-label learning. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.688-697. [code][data]

Y. Zhang and Z.-H. Zhou. Multi-label dimensionality reduction via dependency maximization. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08), Chicago, IL, 2008, pp1503-1505. (short paper) [code & data]

M.-L. Zhang and Z.-H. Zhou. Multi-label learning by instance differentiation. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI'07), Vancouver, Canada, 2007, pp.669-674. [code]

M.-L. Zhang and Z.-H. Zhou. ML-kNN: A lazy learning approach to multi-label learning. Pattern Recognition, 2007, 40(7): 2038-2048. [code]

Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In: Advances in Neural Information Processing Systems 19 (NIPS'06) (Vancouver, Canada), B. Schölkopf, J. C. Platt, and T. Hofmann, eds. Cambridge, MA: MIT Press, 2007, pp.1609-1616. [code][data]

M.-L. Zhang and Z.-H. Zhou. Multilabel neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10): 1338-1351. [code]

M.-L. Zhang and Z.-H. Zhou. A k-nearest neighbor based algorithm for multi-label classification. In: Proceedings of the 1st IEEE International Conference on Granular Computing (GrC'05), Beijing, China, 2005, pp.718-721.

[go top]

Multi-Instance Learning

Y.-L. Zhang and Z.-H. Zhou. Multi-instance learning with key instance shift. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.3441-3447. [code]

J. Feng and Z.-H. Zhou. DeepMIML network. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017, pp.1884-1890.

D.-C. Zhan, J. Tang, and Z.-H. Zhou. Online game props recommendation with real assessments. Complex & Intelligent Systems, 2017, 3(1): 1-15.

X.-S. Wei, J. Wu, and Z.-H. Zhou. Scalable algorithms for multi-instance learning. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(4): 975-987. [code]

X.-S. Wei and Z.-H. Zhou. An empirical study on image bag generators for multi-instance learning. Machine Learning, 2016, 105(2): 155-198. [code]

X.-S. Wei, J. Wu, and Z.-H. Zhou. Scalable multi-instance learning. In: Proceedings of the 14th IEEE International Conference on Data Mining (ICDM'14), Shenzhen, China, 2014, pp.1037-1042. [code]

Y. Zhu, J. Wu, Y. Jiang, and Z.-H. Zhou. Learning with augmented multi-instance view. In: Proceedings of the 6th Asian Conference on Machine Learning (ACML'14), Nha Trang, Vietnam, 2014, JMLR: W&CP 39, pp.234-249.

W.-J. Zhang and Z.-H. Zhou. Multi-instance learning with distribution change. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), Quebec City, Canada, 2014, pp.2184-2190.

Y.-F. Li, I. W. Tsang, J. T. Kwok, and Z.-H. Zhou. Convex and scalable weakly labeled SVMs. Journal of Machine Learning Research, 2013, 14: 2151-2188. (CORR abs/1303.1271) [code]

G. Liu, J. Wu and Z.-H. Zhou. Key instance detection in multi-instance learning. In: Proceedings of the 4th Asian Conference on Machine Learning (ACML'12), Singapore, 2012, JMLR: W&CP 25, pp.253-268.

W. Wang and Z.-H. Zhou. Learnability of multi-instance multi-label learning. Chinese Science Bulletin, 2012, 57(19): 2488-2491.

Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, and Y.-F. Li. Multi-instance multi-label learning. Artificial Intelligence, 2012, 176(1): 2291-2320. (CORR abs/0808.3231) [supplement] [code]

Y.-Y. Sun, M. Ng, and Z.-H. Zhou. Multi-instance dimensionality reduction. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA, 2010, pp.587-592.

Y.-F. Li, J. T. Kwok, I. W. Tsang, and Z.-H. Zhou. A convex method for locating regions of interest with multi-instance learning. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'09), Bled, Slovenia, Part II, LNAI 5782, 2009, pp.15-30. [code]

Z.-H. Zhou, Y.-Y. Sun, and Y.-F. Li. Multi-instance learning by treating instances as non-i.i.d. samples. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1249-1256. (CORR abs/0807.1997) [code][data]

M.-L. Zhang and Z.-H. Zhou. Multi-instance clustering with applications to multi-instance prediction. Applied Intelligence, 2009, 31(1): 47-68. [code]

Z.-H. Zhou and J.-M. Xu. On the relation between multi-instance learning and semi-supervised learning. In: Proceedings of the 24th International Conference on Machine Learning (ICML'07), Corvallis, OR, 2007, pp.1167-1174. [slides][code]

Z.-H. Zhou and M.-L. Zhang. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155-170. [code]

M.-L. Zhang and Z.-H. Zhou. Adapting RBF neural networks to multi-instance learning. Neural Processing Letters, 2006, 23(1): 1-26. [code]

Z.-H. Zhou. Multi-instance learning from supervised view. Journal of Computer Science and Technology, 2006, 21(5): 800-809. Invited paper

Z.-H. Zhou, X.-B. Xue, and Y. Jiang. Locating regions of interest in CBIR with multi-instance learning techniques. In: Proceedings of the 18th Australian Joint Conference on Artificial Intelligence (AJCAI'05), Sydney, Australia, LNAI 3809, 2005, pp.92-101.

Z.-H. Zhou, K. Jiang, and M. Li. Multi-instance learning based web mining. Applied Intelligence, 2005, 22(2): 135-147. [data]

Z.-H. Zhou. Multi-instance learning: A survey. Technical Report, AI Lab, Department of Computer Science & Technology, Nanjing University, Nanjing, China, Mar. 2004.

M.-L. Zhang and Z.-H. Zhou. Improve multi-instance neural networks through feature selection. Neural Processing Letters, 2004, 19(1): 1-10. [code]

Z.-H. Zhou and M.-L. Zhang. Ensembles of multi-instance learners. In: Proceedings of the 14th European Conference on Machine Learning (ECML'03), Cavtat-Dubrovnik, Croatia, LNAI 2837, 2003, pp.492-502. [code]

Z.-H. Zhou, M.-L. Zhang, and K.-J. Chen. A novel bag generator for image database retrieval with multi-instance learning techniques. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), Sacramento, CA, 2003, pp.565-569.

Z.-H. Zhou and M.-L. Zhang. Neural networks for multi-instance learning. Technical Report, AI Lab, Department of Computer Science & Technology, Nanjing University, Nanjing, China, Aug. 2002. [code]

[go top]

Multi-View Learning

P. Zhao, Y. Jiang, and Z.-H. Zhou. Multi-view matrix completion for clustering with side information. In: Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'17), Jeju, Korea, 2017, pp.403-415.

H.-J. Ye, D.-C. Zhan, X.-M. Si, Y. Jiang, and Z.-H. Zhou. What makes objects similar: A unified multi-metric learning approach. In: Advances in Neural Information Processing Systems 29 (NIPS'16) (Barcelona, Spain), D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, R. Garnett, eds. Cambridge, MA: MIT Press, 2016, pp.1235-1243. [code]

Y. Zhu, W. Gao, and Z.-H. Zhou. One-pass multi-view learning. In: Proceedings of the 7th Asian Conference on Machine Learning (ACML'15), Hong Kong, 2015, JMLR: W&CP 45, pp.407-422.

H.-J. Ye, D.-C. Zhan, Y. Miao, Y. Jiang, and Z.-H. Zhou. Rank consistency based multi-view learning: A privacy-preserving approach. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM'15), Melbourne, Australia, 2015, pp.991-1000. [code]

J. Liu, Y. Jiang, Z. Li, Z.-H. Zhou, and H. Lu. Partially shared latent factor learning with multiview data. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(6): 1233-1246.

S.-Y. Li, Y. Jiang, and Z.-H. Zhou. Partial multi-view clustering. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), Quebec City, Canada, 2014, pp.1968-1974. [code]

W. Wang and Z.-H. Zhou. Co-training with insufficient views. In: Proceedings of the 5th Asian Conference on Machine Learning (ACML'13), Canberra, Australia, 2013, JMLR: W&CP 29, pp.467-482.

B. Wang, J. Jiang, W. Wang, Z.-H. Zhou, and Z. Tu. Unsupervised metric fusion by cross diffusion. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'12), Providence, RI, 2012, pp.2997-3004.

Z.-H. Zhou. Unlabeled data and multiple views. In: Proceedings of the 1st IAPR TC3 Workshop on Partially Supervised Learning (PSL'11), Ulm, Germany, LNAI 7081, 2012, pp.1-7. Keynote Speech at PSL'11

J. Du, C. X. Ling, and Z.-H. Zhou. When does co-training work in real data? IEEE Transactions on Knowledge and Data Engineering, 2011, 23(5): 788-799.

M.-L. Zhang and Z.-H. Zhou. CoTrade: Confident co-training with data editing. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2011, 41(6): 1612-1626. [code]

Z.-H. Zhou. When semi-supervised learning meets ensemble learning. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 6-16.

W. Wang and Z.-H. Zhou. Multi-view active learning in the non-realizable case. In: Advances in Neural Information Processing Systems 23 (NIPS'10) (Vancouver, Canada), J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta, eds. Cambridge, MA: MIT Press, 2010, 2388-2396. (CORR abs/1005.5581) [supplement]

W. Wang and Z.-H. Zhou. A new analysis of co-training. In: Proceedings of the 27th International Conference on Machine Learning (ICML'10), Haifa, Israel, 2010, pp.1135-1142.

Y. Fu, Y. Guo, Y. Zhu, F. Liu, C. Song, and Z.-H. Zhou. Multi-view video summarization. IEEE Transactions on Multimedia, 2010, 12(7): 717-729. [demo]

Z.-H. Zhou and M. Li. Semi-supervised learning by disagreement. Knowledge and Information Systems, 2010, 24(3): 415-439.

M. Li, X.-B. Xue, and Z.-H. Zhou. Exploiting multi-modal interactions: A unified framework. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1120-1125.

Z.-H. Zhou. When semi-supervised learning meets ensemble learning. In: Proceedings of the 8th International Workshop on Multiple Classifier Systems (MCS'09), Reykjavik, Iceland, LNCS 5519, 2009, pp.529-538. [slides] Invited plenary talk at MCS'09

C. X. Ling, J. Du, and Z.-H. Zhou. When does co-training work in real data? In: Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'09), Bangkok, Thailand, LNAI 5476, 2009, pp.596-603.

M. Li, H. Li, and Z.-H. Zhou. Semi-supervised document retrieval. Information Processing & Management, 2009, 45(3): 341-355.

W. Wang and Z.-H. Zhou. On multi-view active learning and the combination with semi-supervised learning. In: Proceedings of the 25th International Conference on Machine Learning (ICML'08), Helsinki, Finland, 2008, pp.1152-1159.

Z.-H. Zhou, D.-C. Zhan, and Q. Yang. Semi-supervised learning with very few labeled training examples. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI'07), Vancouver, Canada, 2007, pp.675-680. [code]

D. Zhang, Z.-H. Zhou, and S. Chen. Semi-supervised dimensionality reduction. In: Proceedings of the 7th SIAM International Conference on Data Mining (SDM'07), Minneapolis, MN, 2007, pp.629-634. [code]

Z.-H. Zhou and M. Li. Semisupervised regression with co-training style algorithms. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(11): 1479-1493. [code]

Z.-H. Zhou. Learning with unlabeled data and its application to image retrieval. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Guilin, China, LNAI 4099, 2006, pp.5-10. Keynote speech at PRICAI'06

Z.-H. Zhou, K.-J. Chen, and H.-B. Dai. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems, 2006, 24(2): 219-244.

Z.-H. Zhou and M. Li. Semi-supervised regression with co-training. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05), Edinburgh, Scotland, 2005, pp.908-913.

Z.-H. Zhou and M. Li. Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(11): 1529-1541. [code]

Z.-H. Zhou, K.-J. Chen, and Y. Jiang. Exploiting unlabeled data in content-based image retrieval. In: Proceedings of the 15th European Conference on Machine Learning (ECML'04), Pisa, Italy, LNAI 3201, 2004, pp.525-536.

[go top]

Semi-Supervised and Active Learning

Y.-L. Zhang, L. Li, J. Zhou, X. Li, and Z.-H. Zhou. Anomaly detection with partially observed anomalies. In: Proceedings of the International World-Wide Web Conference (WWW'18), Big Web Track, Lyon, France, 2018.

H.-C. Dong, Y.-F. Li, and Z.-H. Zhou. Learning from semi-supervised weak-label data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018.

Y.-L. Zhang, L. Li, J. Zhou, X. Li, Y. Liu, Y. Zhang, and Z.-H. Zhou. A PU learning based system for potential malicious URL detection. In: Proceedings of the 24th ACM SIGSAC Conference on Computer and Communications Security (CCS'17), Dallas, TX, 2017, pp.2599-2601. (poster)

B.-J. Hou, L. Zhang, and Z.-H. Zhou. Storage fit learning with unlabeled data. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.1844-1850. [code]

S.-J. Huang, J.-L. Chen, X. Mu, and Z.-H. Zhou. Cost-effective active learning from diverse labelers. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.1879-1885. [code]

Y.-F. Li, H.-W. Zha, and Z.-H. Zhou. Construct safe prediction from multiple regressors. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017, pp.2217-2223. [code]

Y.-F. Li, S.-B. Wang, and Z.-H. Zhou. Graph quality judgement: A large margin expedition. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, pp.1725-1731. [code]

L. Liu, T. G. Dietterich, N. Li, and Z.-H. Zhou. Transductive optimization of top k precision. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, pp.1781-1787. (CORR abs/1510.05976)

Y.-H. Zhou and Z.-H. Zhou. Large margin distribution learning with cost interval and unlabeled data. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(7): 1749-1763. [code]

Y.-F. Li, J. Kwok, and Z.-H. Zhou. Towards safe semi-supervised learning for multivariate performance measures. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016.

W. Gao, L. Wang, Y.-F. Li, and Z.-H. Zhou. Risk minimization in the presence of label noise. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016.

J. Zhong, K. Tang, and Z.-H. Zhou. Active learning from crowds with unsure option. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.1061-1067.

J.-H. Hu, D.-C. Zhan, X. Wu, Y. Jiang, and Z.-H. Zhou. Pairwised specific distance learning from physical linkages. ACM Transactions on Knowledge Discovery from Data, 2015, 9(3): Article 20.

J. Liu, Y. Jiang, Z. Li, Z.-H. Zhou, and H. Lu. Partially shared latent factor learning with multiview data. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(6): 1233-1246.

Y.-F. Li and Z.-H. Zhou. Towards making unlabeled data never hurt. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 175-188. [code]

Q. Da, Y. Yu, and Z.-H. Zhou. Learning with augmented class by exploiting unlabeled data. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), Quebec City, Canada, 2014, pp.1760-1766. [code]

W. Wang and Z.-H. Zhou. Co-training with insufficient views. In: Proceedings of the 5th Asian Conference on Machine Learning (ACML'13), Canberra, Australia, 2013, JMLR: W&CP 29, pp.467-482.

Q. Da, Y. Yu, and Z.-H. Zhou. Self-practice imatation learning from weak policy. In: Proceedings of the 2nd International Workshop on Partial Supervised Learning (PSL'13), LNAI 8183, Nanjing, China, 2013, pp.9-20.

J.-S. Wu and Z.-H. Zhou. Sequence-based prediction of microRNA-binding residues in proteins using cost-sensitive laplacian support vector machines. ACM/IEEE Transactions on Computational Biology and Bioinformatics, 2013, 10(3): 752-759. [code]

Y.-F. Li, I. W. Tsang, J. T. Kwok, and Z.-H. Zhou. Convex and scalable weakly labeled SVMs. Journal of Machine Learning Research, 2013, 14: 2151-2188. (CORR abs/1303.1271) [code]

Q. Li, X. Wang, W. Wang, Y. Jiang, Z.-H. Zhou, and W. Tu. Disagreement-based multi-system tracking. In: Proceedings of the ACCV Workshop on Detection and Tracking in Challenging Environments (DTCE'12), in conjunction with ACCV'12, Daejeon, Korea, LNCS 7729, 2013, pp.320-334.

T. Chen, Y. Chen, Q. Guo, Z.-H. Zhou, L. Li, and Z. Xu. Effective and efficient microprocessor design space exploration using unlabeled design configurations. ACM Transactions on Intelligent Systems and Technology, 2013, 5(1): Article 20.

Y. Wang, S. Chen, and Z.-H. Zhou. New semi-supervised classification method based on modified cluster assumption. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(5): 689-702.

M.-L. Zhang and Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. Data Mining and Knowledge Discovery, 2013, 26(1): 98-129. (CORR abs/0909.3593) [code]

X. Kong, M. K. Ng, and Z.-H. Zhou. Transductive multilabel learning via label set propagation. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(3): 704-719. [code]

C. Chen, J. Zhang, X. He, and Z.-H. Zhou. Non-parametric kernel learning with robust pairwise constraints. International Journal of Machine Learning and Cybernetics, 2012, 3(2): 83-96.

M. Li, H. Zhang, R. Wu, and Z.-H. Zhou. Sample-based software defect prediction with active and semi-supervised learning. Automated Software Engineering, 2012, 19(2): 201-230.

Y.-F. Li and Z.-H. Zhou. Towards making unlabeled data never hurt. In: Proceedings of the 28th International Conference on Machine Learning (ICML'11), Bellevue, WA, 2011, pp.1081-1088. [code]

Y.-F. Li and Z.-H. Zhou. Improving semi-supervised support vector machines through unlabeled instances selection. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI'11), San Francisco, CA, 2011, pp.386-391. (CORR abs/1005.1545)

Q. Guo, T. Chen, Y. Chen, Z.-H. Zhou, W. Hu, and Z. Xu. Effective and efficient microprocessor design space exploration using unlabeled design configurations. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp.1671-1677.

M.-L. Zhang and Z.-H. Zhou. CoTrade: Confident co-training with data editing. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2011, 41(6): 1612-1626. [code]

J. Du, C. X. Ling, and Z.-H. Zhou. When does co-training work in real data? IEEE Transactions on Knowledge and Data Engineering, 2011, 23(5): 788-799.

Y. Jiang, M. Li, and Z.-H. Zhou. Software defect detection with ROCUS. Journal of Computer Science and Technology, 2011, 26(2): 328-342.

Z.-H. Zhou. When semi-supervised learning meets ensemble learning. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 6-16. Invited paper

M.-L. Zhang and Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM'10), Sydney, Australia, 2010, pp.609-618. (CORR abs/0909.3593) [code]

S.-J. Huang, R. Jin, and Z.-H. Zhou. Active learning by querying informative and representative examples. In: Advances in Neural Information Processing Systems 23 (NIPS'10) (Vancouver, Canada), J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta, eds. Cambridge, MA: MIT Press, 2010, pp.892-900. [supplement] [code]

W. Wang and Z.-H. Zhou. A new analysis of co-training. In: Proceedings of the 27th International Conference on Machine Learning (ICML'10), Haifa, Israel, 2010, pp.1135-1142.

Y.-F. Li, J. Kwok, and Z.-H. Zhou. Cost-sensitive semi-supervised support vector machine. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA, 2010, pp.500-505. [code]

Z.-H. Zhou and M. Li. Semi-supervised learning by disagreement. Knowledge and Information Systems, 2010, 24(3): 415-439.

J.-M. Xu, G. Fumera, F. Roli, and Z.-H. Zhou. Training SpamAssassin with active semi-supervised learning. In: Proceedings of the 6th Conference on Email and Anti-Spam (CEAS'09), Mountain View, CA, 2009.

Y.-F. Li, J. T. Kwok, and Z.-H. Zhou. Semi-supervised learning using label mean. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.633-640. [code]

D.-C. Zhan, M. Li, Y.-F. Li, and Z.-H. Zhou. Learning instance specific distances using metric propagation. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1225-1232. [code]

Y. Zhang and Z.-H. Zhou. Non-metric label propagation. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1357-1362. [code]

Z.-H. Zhou, M. Ng, Q.-Q. She, and Y. Jiang. Budget semi-supervised learning. In: Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'09), Bangkok, Thailand, LNAI 5476, 2009, pp.588-595.

M. Li, H. Li, and Z.-H. Zhou. Semi-supervised document retrieval. Information Processing & Management, 2009, 45(3): 341-355.

W. Wang and Z.-H. Zhou. Analyzing co-training style algorithms. In: Proceedings of the 18th European Conference on Machine Learning (ECML'07), Warsaw, Poland, LNAI 4701, 2007, pp.454-465.

Z.-H. Zhou and M. Li. Semisupervised regression with co-training style algorithms. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(11): 1479-1493. [code]

M. Li and Z.-H. Zhou. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, 2007, 37(6): 1088-1098. [code]

Z.-H. Zhou, K.-J. Chen, and H.-B. Dai. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems, 2006, 24(2): 219-244.

M. Li and Z.-H. Zhou. SETRED: Self-training with editing. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'05), Hanoi, Vietnam, LNAI 3518, 2005, pp.611-621.

Z.-H. Zhou and M. Li. Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(11): 1529-1541. [code]

[go top]

Cost-Sensitive and Class-Imbalance Learning

Y.-H. Zhou and Z.-H. Zhou. Large margin distribution learning with cost interval and unlabeled data. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(7): 1749-1763. [code]

W. Gao, L. Wang, R. Jin, S.-H. Zhu, and Z.-H. Zhou. One-pass AUC optimization. Artificial Intelligence, 2016, 236: 1-29. [code]

W. Gao and Z.-H. Zhou. On the consistency of AUC pairwise optimization. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.939-945. (CORR abs/1208.0645)

N. Li, R. Jin, and Z.-H. Zhou. Top rank optimization in linear time. In: Advances in Neural Information Processing Systems 27 (NIPS'14) (Montreal, Canada), Z. GhahramaM. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger, eds. Cambridge, MA: MIT Press, 2014, pp. 1502-1510. (CORR abs/1410.1462) [code]

X.-Y. Liu, Q.-Q. Li, and Z.-H. Zhou. Learning imbalanced multi-class data with optimal dichotomy weights. In: Proceedings of the 13th IEEE International Conference on Data Mining (ICDM'13), Dallas, TX, 2013, pp.478-487.

W. Gao, R. Jin, S. Zhu, and Z.-H. Zhou. One-pass AUC optimization. In: Proceedings of the 30th International Conference on Machine Learning (ICML'13), Atlanta, GA, 2013, JMLR: W&CP 28(3), pp.906-914. (CORR abs/1305.1363) [code]

X.-Y. Liu and Z.-H. Zhou. Imbalanced learning. In: H. He, Y. Ma, eds. Imbalanced Learning: Foundations, Algorithms, and Applications, Hoboken, NJ: Wiley-IEEE, 2013, 61-82.

W. Gao and Z.-H. Zhou. On the consistency of AUC optimization. CORR abs/1208.0645, 2012.

T. R. Hoens, Q. Qian, N. V. Chawla, and Z.-H. Zhou. Building decision trees for the multiclass imbalance problem. In: Proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'12), LNAI 7301, Kuala Lumpur, Malaysia, 2012, pp.122-134.

X.-Y. Liu and Z.-H. Zhou. Towards cost-sensitive learning for real-world applications. In: Proceedings of the PAKDD 2011 International Workshops, LNAI 7104, Shenzhen, China, 2012, pp.494-505.

Y. Jiang, M. Li, and Z.-H. Zhou. Software defect detection with ROCUS. Journal of Computer Science and Technology, 2011, 26(2): 328-342.

X.-Y. Liu and Z.-H. Zhou. Learning with cost intervals. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'10), Washington, DC, 2010, pp.403-412. [code]

Y. Zhang and Z.-H. Zhou. Cost-sensitive face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(10): 1758-1769. [code]

Z.-H. Zhou and X.-Y. Liu. On multi-class cost-sensitive learning. Computational Intelligence, 2010, 26(3): 232-257. [code]

X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2009, 39(2): 539-550. [code]

L.-P. Liu, Y. Yu, Y. Jiang, and Z.-H. Zhou. TEFE: A time-efficient approach to feature extraction. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.423-432.

Y. Zhang and Z.-H. Zhou. Cost-sensitive face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'08), Anchorage, AK, 2008. [code]

Z.-H. Zhou and X.-Y. Liu. On multi-class cost-sensitive learning. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06), Boston, MA, 2006, pp.567-572. [code]

X.-Y. Liu and Z.-H. Zhou. The influence of class imbalance on cost-sensitive learning: An empirical study. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.970-974.

X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory under-sampling for class-imbalance learning. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.965-969. [code]

Z.-H. Zhou and X.-Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1): 63-77. [code]

[go top]

Metric Learning, Dimensionality Reduction and Feature Selection

H.-J. Ye, D.-C. Zhan, Y. Jiang, and Z.-H. Zhou. What makes objects similar: A unified multi-metric learning approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press. [code]

C. Hou, Y. Jiao, F. Nie, T. Luo, and Z.-H. Zhou. 2D feature selection by sparse matrix regression. IEEE Transactions on Image Processing, 2017, 26(9): 4255-4268.

W.-C. Kang, W.-J. Li, and Z.-H. Zhou. Column sampling based discrete supervised hashing. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016.

L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou. Stochastic optimization for kernel PCA. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016.

D.-C. Zhan, P. Hu, Z. Chu, and Z.-H. Zhou. Learning expected hitting time distance. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016.

J.-H. Hu, D.-C. Zhan, X. Wu, Y. Jiang, and Z.-H. Zhou. Pairwised specific distance learning from physical linkages. ACM Transactions on Knowledge Discovery from Data, 2015, 9(3): Article 20.

J. Zhang, Q. Wang, L. He, and Z.-H. Zhou. Quantitative analysis of nonlinear embedding. IEEE Transactions on Neural Networks, 2011, 22(12): 1987-1998. [code]

X. Geng, K. Smith-Miles, Z.-H. Zhou, and L. Wang. Face image modeling by multilinear subspace analysis with missing values. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2011, 41(3): 881-892.

Y.-Y. Sun, M. Ng, and Z.-H. Zhou. Multi-instance dimensionality reduction. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA, 2010, pp.587-592.

Y. Zhang and Z.-H. Zhou. Multi-label dimensionality reduction via dependence maximization. ACM Transactions on Knowledge Discovery from Data, 2010, 4(3): Article 14. [code & data]

J. Liu, S. Chen, Z.-H. Zhou, and X. Tan. Generalized low rank approximations of matrices revisited. IEEE Transactions on Neural Networks, 2010, 21(4): 621-632.

L.-P. Liu, Y. Jiang, and Z.-H. Zhou. Least square incremental linear discriminant analysis. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM'09), Miami, FL, 2009, pp.298-306. [code]

X. Geng, K. Smith-Miles, Z.-H. Zhou, and L. Wang. Face image modeling by multilinear subspace analysis with missing values. In: Proceedings of the 17th ACM International Conference on Multimedia (MM'09), Beijing, China, 2009, pp.629-632. (short paper)

Y. Zhang and Z.-H. Zhou. Non-metric label propagation. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1357-1362.

D. Zhang, S. Chen, and Z.-H. Zhou. Constraint score: A new filter method for feature selection with pairwise constraints. Pattern Recognition, 2008, 41(5): 1440-1451.

D. Zhang, Z.-H. Zhou, and S. Chen. Adaptive kernel principal component analysis with unsupervised learning of kernels. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.1178-1182. [code]

D.-C. Zhan and Z.-H. Zhou. Neighbor line-based locally linear embedding. In: Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'06), Singapore, LNAI 3918, 2006, pp.606-615.

D. Zhang, Z.-H. Zhou, and S. Chen. Non-negative matrix factorization on kernels. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Guilin, China, LNAI 4099, 2006, pp.404-412. [data] This paper won the Best Paper Award at PRICAI'06

J. Zhang, L. He, and Z.-H. Zhou. Ensemble-based discriminant manifold learning for face recognition. In: Proceedings of the 2nd International Conference on Natural Computation (ICNC'06), Chongqing, China, LNCS 4221, 2006, pp.29-38.

D. Zhang, Z.-H. Zhou, and S. Chen. Diagonal principal component analysis for face recognition. Pattern Recognition, 2006, 39(1): 140-142. [data]

D. Zhang, S. Chen, and Z.-H. Zhou. Two-dimensional non-negative matrix factorization for face representation and recognition. In: Proceedings of the 2nd International Workshop on Analysis and Modeling of Faces and Gestures (AMFG'05), in Conjunction with ICCV'05, Beijing, China, LNCS 3723, 2005, pp.350-363. [data]

X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, 35(6): 1098-1107. [code]

D. Zhang and Z.-H. Zhou. (2D)²PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition. Neurocomputing, 2005, 69(1-3): 224-231. [data]

J. Zhang, H. Shen, and Z.-H. Zhou. Unified locally linear embedding and linear discriminant analysis algorithm (ULLELDA) for face recognition. In: Proceedings of the 5th Chinese Conference on Biometric Recognition (Sinobiometrics'04), Guangzhou, China, LNCS 3338, 2004, pp.296-304.

[go top]

Ensemble Learning

Z.-H. Zhou. Ensemble Methods: Foundations and Algorithms, Boca Raton, FL: Chapman & Hall/CRC, 2012. (ISBN 978-1-439-830031) [TOC; Sample chapters: Chapter 2, Chaper 6; Japanese translation]

T. Sun and Z.-H. Zhou. Structural diversity of decision tree ensemble learning. Frontiers of Computer Science, in press.

J. Feng and Z.-H. Zhou. AutoEncoder by forest. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018. (CORR abs/1709.09018)

T. Zhang and Z.-H. Zhou. Optimal margin distribution clustering. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018.

Z.-H. Zhou and J. Feng. Deep forest: Towards an alternative to deep neural networks. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.3553-3559. (CORR abs/1702.08835) [code]

X. Mu, K. M. Ting, and Z.-H. Zhou. Classification under streaming emerging new classes: A solution using completely-random trees. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(8): 1605-1618. (CORR abs/1605.09131) [code]

N. Li, Y. Jiang, and Z.-H. Zhou. Multi-label selective ensemble. In: Proceedings of the 12th International Workshop on Multiple Classifier Systems (MCS'15), LNCS 9132, Ulm, Germany, 2015, pp.76-88. [code]

C. Qian, Y. Yu, and Z.-H. Zhou. Pareto ensemble pruning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), Austin, TX, 2015, pp. 2935-2941. [code]

Q. Guo, T. Chen, Z.-H. Zhou, O. Temam, L. Li, D. Qian, and Y. Chen. Robust design space modeling. ACM Transactions on Design Automation of Electronic Systems, 2015, 20(2): Article 18.

W.-Z. Dai and Z.-H. Zhou. Inductive logic boosting. CORR abs/1402.6077, 2014.

Z.-H. Zhou. Large margin distribution learning. In: Proceedings of the 6th IAPR International Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR'14), Montreal, Canada, LNAI 8774, 2014, pp.1-11. (keynote article) [code][slides]

J. Zhang, P. S. Yu and Z.-H. Zhou. Meta-path based multi-network collective link prediction. In: Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'14), New York, NY, 2014, pp.1286-1295.

T. Chen, Q. Guo, K. Tang, O. Temam, Z. Xu, Z.-H. Zhou, and Y. Chen. ArchRanker: A ranking approach to design space exploration. In: Proceedings of the 41st International Symposium on Computer Architecture (ISCA'14), Minneapolis, MN, 2014, pp.85-96.

Q. Da, Y. Yu, and Z.-H. Zhou. Napping for functional represenation of policy. In: Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'14), Paris, France, 2014, pp.189-196. [code]

L. Yuan, C. Pan, S. Ji, M. McCutchan, Z.-H. Zhou, S. J. Newfeld, S. Kumar, and J. Ye. Automated annotation of developmental stages of drosophila embryos in images containing spatial patterns of expression. Bioinformatics, 2014, 30(2): 266-273.

W. Gao and Z.-H. Zhou. On the doubt about margin explanation of boosting. Artificial Intelligence, 2013, 203: 1-18. (CORR abs/1009.3613)

N. Li and Z.-H. Zhou. Selective ensemble of classifier chains. In: Proceedings of the 11th International Workshop on Multiple Classifier Systems (MCS'13), LNCS 7872, Nanjing, China, 2013, pp.146-156.

N. Li, I. W. Tsang, and Z.-H. Zhou. Efficient optimization of performance measures by classifier adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1370-1382. (CORR abs/1012.0930) [code]

X.-Y. Liu and Z.-H. Zhou. Imbalanced learning. In: H. He, Y. Ma, eds. Imbalanced Learning: Foundations, Algorithms, and Applications, Hoboken, NJ: Wiley-IEEE, 2013, 61-82.

C. He, Y.-X. Li, G. Zhang, Z. Gu, R. Yang, J. Li, Z. J. Lu, Z.-H. Zhou, C. Zhang, and J. Wang. MiRmat: Mature microRNA sequence prediction. PLOS One, 2012, 7(12): e51673.

N. Li, Y. Yu, and Z.-H. Zhou. Diversity regularized ensemble pruning. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'12), Bristol, UK, LNCS 7523, 2012, pp330-345. [code]

M.-L. Zhang and Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. Data Mining and Knowledge Discovery, 2013, 26(1): 98-129. (CORR abs/0909.3593) [code]

F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data, 2012, 6(1): Article 3. [code]

D. Zhang, N. Li, Z.-H. Zhou, C. Chen, L. Sun, and S. Li. iBAT: Detecting anomalous taxi trajectories from GPS traces. In: Proceedings of the 13th ACM International Conference on Ubiquitous Computing (UbiComp'11), Beijing, China, 2011, pp.99-108.

Y. Yu, Y.-F. Li, and Z.-H. Zhou. Diversity regularized machine. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp.1603-1608. [code]

Y. Jiang, M. Li, and Z.-H. Zhou. Software defect detection with ROCUS. Journal of Computer Science and Technology, 2011, 26(2): 328-342.

L. Wang, M. Sugiyama, Z. Jing, C. Yang, Z.-H. Zhou, and J. Feng. A refined margin analysis for boosting algorithms via equilibrium margin. Journal of Machine Learning Research, 2011, 12: 1835-1863.

Z.-H. Zhou. When semi-supervised learning meets ensemble learning. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 6-16. Invited paper

T. F. Liu, K. M. Ting, and Z.-H. Zhou. On detecting clustered anomalies using SCiForest. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'10), Barcelona, Spain, LNAI 6322, 2010, pp.274-290.

W. Gao and Z.-H. Zhou. Approximation stability and boosting. In: Proceedings of the 21st International Conference on Algorithmic Learning Theory (ALT'10), LNCS 6331, Canberra, Australia, 2010, pp.59-73.

Z.-H. Zhou and N. Li. Multi-information ensemble diversity. In: Proceedings of the 9th International Workshop on Multiple Classifier Systems (MCS'10), LNCS 5997, Cairo, Egypt, 2010, pp.134-144. [code]

Y. Yu and Z.-H. Zhou. A framework for modeling positive class expansion with single snapshot. Knowledge and Information Systems, 2010, 25(2): 211-227. [slides][code&data] Invited paper for the PAKDD'08 Best Paper Award

M. Li, W. Wang, and Z.-H. Zhou. Exploiting remote learners in internet environment with agents. Science China Information Sciences, 2010, 53(1): 64-76.

N. Li, Y. Yu, and Z.-H. Zhou. Semi-naive exploitation of one-dependence estimators. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM'09), Miami, FL, 2009, pp.278-287.

N. Li and Z.-H. Zhou. Selective ensemble under regularization framework. In: Proceedings of the 8th International Workshop on Multiple Classifier Systems (MCS'09), Reykjavik, Iceland, LNCS 5519, 2009, pp.293-303. [code]

X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2009, 39(2): 539-550. [code]

Z.-H. Zhou. Ensemble learning. In: S. Z. Li ed. Encyclopedia of Biometrics, Berlin: Springer, 2009, 270-273.

Z.-H. Zhou. Ensemble. In: L. Liu and T. Özsu eds. Encyclopedia of Database Systems, Berlin: Springer, 2009, 988-991.

Z.-H. Zhou. Boosting. In: L. Liu and T. Özsu eds. Encyclopedia of Database Systems, Berlin: Springer, 2009, 260-263.

Z.-H. Zhou and Yang Yu. AdaBoost. In: X. Wu and V. Kumar eds. The Top Ten Algorithms in Data Mining, Boca Raton, FL: Chapman & Hall, 2009, 127-149.

Y. Jiang, M. Li, and Z.-H. Zhou. Mining extremely small data sets with application to software reuse. Software: Practice and Experience, 2009, 39(4): 423-440.

F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation forest. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.413-422. [code] This paper won the Theoretical/Algorithms Runner-Up Best Paper Award at IEEE ICDM'08

D. Zhang, S. Chen, Z.-H. Zhou, and Q. Yang. Constraint projections for ensemble learning. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08), Chicago, IL, 2008, pp.758-763.

L. Wang, M. Sugiyama, C. Yang, Z.-H. Zhou, and J. Feng. On the margin explanation of boosting algorithm. In: Proceedings of the 21st Annual Conference on Learning Theory (COLT'08), Helsinki, Finland, 2008, pp.479-490.

Y. Yu and Z.-H. Zhou. A framework for modeling positive class expansion with single snapshot. In: Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, Japan, LNAI 5012, 2008, pp.429-440. [slides][code&data] This paper won the Best Paper Award at PAKDD'08

F. T. Liu, K. M. Ting, Y. Yu, and Z.-H. Zhou. Spectrum of variable-random trees. Journal of Artificial Intelligence Research, 2008, 32: 355-384.

Y. Yu, Z.-H. Zhou, and K. M. Ting. Cocktail ensemble for regression. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM'07), Omeha, NE, 2007, pp.721-726.

Z.-H. Zhou and M.-L. Zhang. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155-170. [code]

Y. Yu, D.-C. Zhan, X.-Y. Liu, M. Li, and Z.-H. Zhou. Predicting future customers via ensembling gradually expanded trees. International Journal of Data Warehousing and Mining, 2007, 3(2): 12-21. Invited paper for the PAKDD'06 Data Mining Competition (Open Category) Grand Champion Team

Y. Jiang, M. Li, and Z.-H. Zhou. Generation of comprehensible hypotheses from gene expression data. In: Proceedings of the International Workshop on Data Mining for Biomedical Application (BioDM'06), in conjunction with PAKDD'06, Singapore, LNBI 3916, 2006, pp.116-123.

Z.-H. Zhou and W. Tang. Clusterer ensemble. Knowledge-Based Systems, 2006, 19(1): 77-83. [code]

X. Geng and Z.-H. Zhou. Image region selection and ensemble for face recognition. Journal of Computer Science and Technology, 2006, 21(1): 116-125. [data]

Z.-H. Zhou. Multi-instance learning from supervised view. Journal of Computer Science and Technology, 2006, 21(5): 800-809. Invited paper

X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft kNN ensemble. IEEE Transactions on Neural Networks, 2005, 16(4): 875-886. [code][data]

Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, 35(4): 725-735. [code]

Y. Jiang and Z.-H. Zhou. Editing training data for kNN classifiers with neural network ensemble. In: Proceedings of the 1st International Symposium on Neural Networks (ISNN'04), Dalian, China, LNCS 3173, 2004, pp.356-361.

Z.-H. Zhou and Y. Jiang. NeC4.5: Neural ensemble based C4.5. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(6): 770-773. [code]

Z.-H. Zhou and W. Tang. Selective ensemble of decision trees. In: Proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC'03), Chongqing, China, LNAI 2639, 2003, pp.476-483.

Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, 7(1): 37-42. [code]

Z.-H. Zhou, Y. Jiang, and S.-F. Chen. Extracting symbolic rules from trained neural network ensembles. AI Communications, 2003, 16(1): 3-15.

Z.-H. Zhou, J. Wu, and W. Tang. Ensembling neural networks: Many could be better than all. Artificial Intelligence, 2002, 137(1-2): 239-263. [code]

Z.-H. Zhou, Y. Jiang, Y.-B. Yang, and S.-F. Chen. Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine, 2002, 24(1): 25-36.

Z.-H. Zhou, J.-X. Wu, Y. Jiang, and S.-F. Chen. Genetic algorithm based selective neural network ensemble. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI'01), Seattle, WA, 2001, vol.2, pp.797-802. This paper was nominated along with other four papers selected from 796 submissions for the Distinguished Paper Award at IJCAI'01

[go top]

Structure Learning and Clustering

T. Zhang and Z.-H. Zhou. Optimal margin distribution clustering. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018.

Y. Fu, H. Xiong, Y. Ge, Y. Zheng, Z. Yao, and Z.-H. Zhou. Modeling of geographical dependencies for real estate appraisal. ACM Transactions on Knowledge Discovery from Data, 2016, 11(1): Article 11.

Y.-T. Qiang, Y. Fu, Y. Guo, Z.-H. Zhou, and L. Sigal. Learning to generate posters of scientic papers. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016.

M. Xu, R. Jin, and Z.-H. Zhou. CUR algorithm for partially observed matrices. In: Proceedings of the 32nd International Conference on Machine Learning (ICML'15), Lille, France, 2015. (CORR abs/1411.0860)

L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou. A simple homotopy algorithm for compressive sensing. In: Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS'15), San Diego, CA, 2015, JMLR: W&CP 38, pp.1116-1124. [supplement]

X.-Y. Dai, J.-B. Zhang, S.-J. Huang, J.-J. Chen, and Z.-H. Zhou. Structured sparsity with group-graph regularization. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), Austin, TX, 2015, pp.1714-1720.

L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou. Online bandit learning with non-convex losses. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), Austin, TX, 2015, pp.3158-3164.

J.-H. Hu, D.-C. Zhan, X. Wu, Y. Jiang, and Z.-H. Zhou. Pairwised specific distance learning from physical linkages. ACM Transactions on Knowledge Discovery from Data, 2015, 9(3): Article 20.

T. Zhang and Z.-H. Zhou. Large margin distribution machine. In: Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'14), New York, NY, 2014, pp.313-322. (CORR abs/1311.0989) [code]

C.-M. Huang, J. J.-C. Ying, V. S. Tseng, and Z.-H. Zhou. Location semantics prediction for living analytics by mining smartphone data. In: Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA'14), Shanghai, China, 2014, pp.527-533.

Y. Fu, H. Xiong, Y. Ge, Z. Yao, Y. Zheng, and Z.-H. Zhou. Exploiting geographic dependencies for real estate appraisal: A mutual perspective of ranking and clustering. In: Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'14), New York, NY, 2014, pp.1047-1056.

W. Gao and Z.-H. Zhou. Dropout Rademacher complexity of deep neural networks. CORR abs/1402.3811, 2014.

K. Zhang, B. Schölkopf, K. Muandet, Z. Wang, Z.-H. Zhou, and C. Persello. Single-source domain adaptation with target and conditional shift. In: J. A. K. Suykens, M. Signoretto, A. Argyriou, eds. Regularization, Optimization, Kernels, and Support Vector Machines, Boca Raton, FL: CRC Press, 2014, 428-456.

C. Chen, D. Zhang, N. Li, and Z.-H. Zhou. B-Planner: Planning bidirectional night bus routes using large-scale taxi GPS traces. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(4): 1451-1465.

Y.-F. Li, I. W. Tsang, J. T. Kwok, and Z.-H. Zhou. Convex and scalable weakly labeled SVMs. Journal of Machine Learning Research, 2013, 14: 2151-2188. (CORR abs/1303.1271) [code]

R. Jin, T. Yang, M. Mahdavi, Y.-F. Li, and Z.-H. Zhou. Improved bounds for the Nyström method with application to kernel classification. IEEE Transactions on Information Theory, 2013, 59(10): 6939-6949.

C. Chen, D. Zhang, Z.-H. Zhou, N. Li, T. Atmaca, S. Li. B-Planner: Night bus route planning using large-scale taxi GPS traces. In: Proceedings of the 11th IEEE International Conference on Pervasive Computing and Communications (PerCom'13), San Diego, CA, 2013, pp.225-233.

L. Wu, X. Wu, A. Lu, and Z.-H. Zhou. A spectral approach to detecting subtle anomalies in graphs. Journal of Intelligent Information Systems, 2013, 41(2): 313-337.

T. Yang, Y.-F. Li, M. Mahdavi, R. Jin and Z.-H. Zhou. Nyström method vs random Fourier features: A theoretical and empirical comparison. In: Advances in Neural Information Processing Systems 25 (NIPS'12) (Lake Tahoe, NV), P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger, eds. Cambridge, MA: MIT Press, 2012, pp.485-493.

S. Wang, Z.-H. Zhou, M. Ge, and C. Wang. Resource allocation for heterogeneous multiuser cognitive radio networks with imperfect spectrum sensing. IEEE Journal on Selected Areas in Communications, 2013, 31(3): 464-475.

L. Yuan, A. Woodard, S. Ji, Y. Jiang, Z.-H. Zhou, S. Kumar, and J. Ye. Learning sparse representation for fruit-fly gene expression pattern image annotation and retrieval. BMC Bioinformatics, 2012, 13: 107.

S. Wang, Z.-H. Zhou, M. Ge, and C. Wang. Resource allocation for heterogeneous multiuser OFDM-based cognitive radio networks with imperfect spectrum sensing. In: Proceedings of the 31st IEEE International Conference on Computer Communications (INFOCOM'12), Orlando, FL, 2012, pp.2264-2272.

L. Wu, X. Ying, X. Wu, A. Lu, and Z.-H. Zhou. Examining spectral space of complex networks with positive and negative links. International Journal of Social Network Mining, 2012, 1(1): 91-111.

F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data, 2012, 6(1): Article 3. [code]

Y. Ge, H. Xiong, C. Liu, and Z.-H. Zhou. A taxi driving fraud detection system. In: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM'11), Vancouver, Canada, 2011, pp.181-190.

W.-Z. Dai, Y. Yu, and Z.-H. Zhou. Lifted-rollout for approximate policy iteration of Markov decision process. In: Proceedings of the 11th IEEE International Conference on Data Mining Workshops (International Workshop on Learning and Data Mining for Robotics (LEMIR'11), in conjunction with ICDM'11), Vancouver, Canada, 2011, pp.689-696.

Y. Wang, Y. Jiang, Y. Wu, and Z.-H. Zhou. Localized K-flats. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI'11), San Francisco, CA, 2011, pp.523-530.

Y. Wang, Y. Jiang, Y. Wu, and Z.-H. Zhou. Local and structural consistency for multi-manifold clustering. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp.1559-1564.

L. Wu, X. Ying, X. Wu, and Z.-H. Zhou. Line orthogonality in adjacency eigenspace with application to community partition. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp.2349-2354.

L. Wu, X. Ying, X. Wu, A. Lu, and Z.-H. Zhou. Spectral analysis of k-balanced signed graphs. In: Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'11), LNAI 6635, Shenzhen, China, 2011, pp.1-12.

S. Wang, F. Huang, and Z.-H. Zhou. Fast power allocation algorithm for cognitive radio networks. IEEE Communications Letters, 2011, 15(8): 845-847.

J. Zhang, Q. Wang, L. He, and Z.-H. Zhou. Quantitative analysis of nonlinear embedding. IEEE Transactions on Neural Networks, 2011, 22(12): 1987-1998. [code]

Y. Wang, Y. Jiang, Y. Wu, and Z.-H. Zhou. Spectral clustering on multiple manifolds. IEEE Transactions on Neural Networks, 2011, 22(7): 1149-1161. [code]

N. Li, I. W. Tsang, and Z.-H. Zhou. Efficiently learning nonlinear classifiers for domain specific performance measures. CORR abs/1012.0930, 2010.

S.-J. Huang, R. Jin, and Z.-H. Zhou. Active learning by querying informative and representative examples. In: Advances in Neural Information Processing Systems 24 (NIPS'10) (Vancouver, Canada), J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta, eds. Cambridge, MA: MIT Press, 2010, pp.892-900. [supplement] [code]

Y. Ge, H. Xiong, Z.-H. Zhou, H. Ozdemir, J. Yu, and K. C. Lee. TOP-EYE: Top-k evolving trajectory outlier detection. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM'10), Toronto, Canada, 2010, pp.1733-1736. (short paper)

Y. Wang, Y. Jiang, Y. Wu, and Z.-H. Zhou. Multi-manifold clustering. In: Proceedings of the 11th Pacific Rim International Conference on Artificial Intelligence (PRICAI'10), LNAI 6230, Daegu, Korea, 2010, pp.280-291. [slides] This paper won the Best Paper Award at PRICAI'10

Y. Fu, Y. Guo, Y. Zhu, F. Liu, C. Song, and Z.-H. Zhou. Multi-view video summarization. IEEE Transactions on Multimedia, 2010, 12(7): 717-729. [demo]

Y. Zhang, R. Jin, and Z.-H. Zhou. Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics, 2010, 1(1): 43-52. Invited paper

Y.-F. Li, I. W. Tsang, J. Kwok, and Z.-H. Zhou. Tighter and convex maximum margin clustering. In: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS'09), Clearwater Beach, FL, 2009, pp.328-335. [code]

M.-L. Zhang and Z.-H. Zhou. Multi-instance clustering with applications to multi-instance prediction. Applied Intelligence, 2009, 31(1): 47-68. [code]

X.-L. Li and Z.-H. Zhou. Structure learning of probabilistic relational models from incomplete relational data. In: Proceedings of the 18th European Conference on Machine Learning (ECML'07), Warsaw, Poland, LNAI 4701, 2007, pp.214-225.

D. Zhang, S. Chen, and Z.-H. Zhou. Entropy-inspired competitive clustering algorithms. International Journal of Software Informatics, 2007, 1(1): 67-84.

Z.-H. Zhou and M.-L. Zhang. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155-170. [code]

Z.-H. Zhou and W. Tang. Clusterer ensemble. Knowledge-Based Systems, 2006, 19(1): 77-83. [code]

[go top]

Crowdsourcing Learning

Y.-X. Ding and Z.-H. Zhou. Crowdsourcing with unsure option. Machine Learning, 2018, 107(4): 749-766. (CORR abs/1609.00292)

W. Wang, X.-Y. Guo, S.-Y. Li, Y. Jiang, and Z.-H. Zhou. Obtaining high-quality label by distinguishing between easy and hard items in crowdsourcing. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.2964-2970.

L. Wang and Z.-H. Zhou. Cost-saving effect of crowdsourcing learning. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, pp.2111-2117.

W. Wang and Z.-H. Zhou. Crowdsourcing label quality: A theoretical study. Science China Information Sciences, 2015, 58(11): 112103.

[go top]

Logic Learning

S. Muggleton, W.-Z. Dai, C. Sammut, A. Tamaddoni-Nezhad, J. Wen, and Z.-H. Zhou. Meta-interpretive learning from noisy images. Machine Learning, in press.

W.-Z. Dai, S. H. Muggleton, J. Wen, A. Tamaddoni-Nezhad, and Z.-H. Zhou. Logic vision: One-shot meta-intepretive learning from real images. In: Proceedings of the 25th International Conference on Inductive Logic Programming (ILP'17), Orleans, France, 2017.

W.-Z. Dai and Z.-H. Zhou. Combining logic abduction and statistical induction: Discovering written primitives with human knowledge. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017, pp.4392-4398.

W.-Z. Dai, S. H. Muggleton, and Z.-H. Zhou. Logic vision: Meta-intepretive learning for simple geometrical concepts. In: Proceedings of the 25th International Conference on Inductive Logic Programming (ILP'15), Kyoto, Japan, 2015.

W.-Z. Dai and Z.-H. Zhou. Statistical unfolded logic learning. In: Proceedings of the 7th Asian Conference on Machine Learning (ACML'15), Hong Kong, 2015, JMLR: W&CP 45, pp. 349-361.

[go top]

Image Retrieval

X.-S. Wei, J.-H. Luo, J. Wu, and Z.-H. Zhou. Selective convolutional descriptor aggregation for fine-grained image retrieval. IEEE Transactions on Image Processing, 2017, 26(6): 2868-2881. [code]

W. Gao and Z.-H. Zhou. Uniform convergence, statiliby and learnability for ranking problems. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI'13), Beijing, China, 2013, pp.1337-1343.

Y. Zhang, R. Jin, and Z.-H. Zhou. Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics, 2010, 1(1): 43-52. Invited paper

Y. Guo, F. Liu, J. Shi, Z.-H. Zhou, and M. Gleicher. Image retargeting using mesh parametrization. IEEE Transactions on Multimedia, 2009, 11(5): 856-867.

Z.-H. Zhou and H.-B. Dai. Query-sensitive similarity measure for content-based image retrieval. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.1211-1215.

Z.-H. Zhou, K.-J. Chen, and H.-B. Dai. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems, 2006, 24(2): 219-244.

A. Ghafoor, Z. Zhang, M. S. Lew, and Z.-H. Zhou. Machine learning approaches to multimedia information retrieval. ACM/Springer Multimedia Systems, 2006, 12(1): 1-2. this article was the editorial to the special issue Machine Learning Approaches to Multimedia Information Retrieval edited by A. Ghafoor, Z. Zhang, M. S. Lew, and Z.-H. Zhou

[go top]

Web Search and Mining

L. Wu, X. Wu, A. Lu, and Z.-H. Zhou. A spectral approach to detecting subtle anomalies in graphs. Journal of Intelligent Information Systems, 2013, 41(2): 313-337.

L. Wu, X. Ying, X. Wu, A. Lu, and Z.-H. Zhou. Examining spectral space of complex networks with positive and negative links. International Journal of Social Network Mining, 2012, 1(1): 91-111.

M. Li, W. Wang, and Z.-H. Zhou. Exploiting remote learners in internet environment with agents. Science China Information Sciences, 2010, 53(1): 64-76.

M. Li, H. Li, and Z.-H. Zhou. Semi-supervised document retrieval. Information Processing & Management, 2009, 45(3): 341-355.

X.-B. Xue and Z.-H. Zhou. Distributional features for text categorization. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(3): 428-442.

M. Li, Z. Zhang, and Z.-H. Zhou. Mining bulletin board systems using community generation. In: Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, Japan, LNAI 5012, 2008, pp.209-221.

X.-B. Xue, Z.-H. Zhou, and Z. Zhang. Improving web search using image snippets. ACM Transactions on Internet Technology, 2008, 8(4): Article 21. [illustration]

Z.-H. Zhou and H.-B. Dai. Exploiting image contents in web search. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI'07), Hyderabad, India, 2007, pp.2928-2933.

X.-B. Xue, Z.-H. Zhou, and Z. Zhang. Improve web search using image snippets. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06), Boston, MA, 2006, pp.1431-1436. [illustration]

X.-B. Xue and Z.-H. Zhou. Distributional features for text categorization. In: Proceedings of the 17th European Conference on Machine Learning (ECML'06), Berlin, Germany, LNAI 4212, 2006, pp.497-508.

Z.-H. Zhou, K. Jiang, and M. Li. Multi-instance learning based web mining. Applied Intelligence, 2005, 22(2): 135-147. [data]

[go top]

Face Recognition

X.-D. Wang and Z.-H. Zhou. Facial age estimation by total order preserving projections. In: Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI'16), LNAI 6230, Phuket, Thailand, 2016, pp.603-615. [code]

F. Song, X. Tan, S. Chen, and Z.-H. Zhou. A literature survey on robust and efficient eye localization in real-life scenarios. Pattern Recognition, 2013, 46(12): 3157-3173.

X. Geng, C. Yin, and Z.-H. Zhou. Facial age estimation by label distribution learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2401-2412.

Y. Zhang and Z.-H. Zhou. Cost-sensitive face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(10): 1758-1769. [code]

X. Tan, F. Song, Z.-H. Zhou, and S. Chen. Enhanced pictorial structures for precise eye localization under uncontrolled conditions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, 2009, pp.1621-1628.

X. Tan, S. Chen, Z.-H. Zhou, and J. Liu. Face recognition under occlusions and variant expressions with partial similarity. IEEE Transactions on Information Forensics & Security, 2009, 4(2): 217-230.

X. Geng, K. Smith-Miles, and Z.-H. Zhou. Facial age estimation by nonlinear aging pattern subspace. In: Proceedings of the 16th ACM International Conference on Multimedia (MM'08), Vancouver, Canada, 2008, pp.721-724. (short paper)

Y. Zhang and Z.-H. Zhou. Cost-sensitive face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'08), Anchorage, AK, 2008. [code]

X. Geng, Z.-H. Zhou, and K. Smith-Miles. Individual stable space: An approach to face recognition under uncontrolled conditions. IEEE Transactions on Neural Networks, 2008, 19(8): 1354-1368.

Liu, S. Chen, Z.-H. Zhou, and X. Tan. Single image subspace for face recognition. In: Proceedings of the 3rd International Workshop on Analysis and Modeling of Faces and Gestures (AMFG'07), in Conjunction with ICCV'07, Rio de Janeiro, Brazil, LNCS 4778, 2007, pp.205-219.

X. Geng, Z.-H. Zhou, and K. Smith-Miles. Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(12): 2234-2240. This paper was listed as the "Featured article" of the vol.29 no.12 issue of TPAMI

X. Tan, S. Chen, Z.-H. Zhou, and J. Liu. Learning non-metric partial similarity based on maximal margin criterion. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), New York, NY, 2006, pp.138-145. [code]

X. Geng, Z.-H. Zhou, Y. Zhang, G. Li, and H. Dai. Learning from facial aging patterns for automatic age estimation. In: Proceedings of the 14th ACM International Conference on Multimedia (MM'06), Santa Barbara, CA, 2006, pp.307-316.

X. Geng, Z.-H. Zhou, and H. Dai. Uncontrolled face recognition by individual stable neural network. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Guilin, China, LNAI 4099, 2006, pp.553-562.

D. Zhang, S. Chen, and Z.-H. Zhou. Recognizing face or object from a single image: Linear vs. kernel methods on 2D patterns. In: Proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition (S+SSPR'06), in conjunction with ICPR'06, Hong Kong, China, LNCS 4109, 2006, pp.889-897. [data]

D. Zhang, Z.-H. Zhou, and S. Chen. Diagonal principal component analysis for face recognition. Pattern Recognition, 2006, 39(1): 140-142. [data]

D. Zhang, S. Chen, and Z.-H. Zhou. Learning the kernel parameters in kernel minimum distance classifier. Pattern Recognition, 2006, 39(1): 133-135.

X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Face recognition from a single image per person: A survey. Pattern Recognition, 2006, 39(9): 1725-1745. This paper won the Pattern Recognition Journal 2006 Best Paper Award Honorable Mention

X. Geng and Z.-H. Zhou. Image region selection and ensemble for face recognition. Journal of Computer Science and Technology, 2006, 21(1): 116-125. [data]

D. Zhang and Z.-H. Zhou. (2D)²PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition. Neurocomputing, 2005, 69(1-3): 224-231. [data]

D. Zhang, S. Chen, and Z.-H. Zhou. A new face recognition method based on SVD perturbation for single example image per person. Applied Mathematics and Computation, 2005, 163(2): 895-907. [data]

X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Robust face recognition from a single training image per person with kernel-based SOM-face. In: Proceedings of the 1st International Symposium on Neural Networks (ISNN'04), Dalian, China, LNCS 3173, 2004, pp.858-863. [data]

Z.-H. Zhou and X. Geng. Projection functions for eye detection. Pattern Recognition, 2004, 37(5): 1049-1056.

S. Chen, J. Liu, and Z.-H. Zhou. Making FLDA applicable to face recognition with one sample per person. Pattern Recognition, 2004, 37(7): 1553-1555. [data]

S. Chen, D. Zhang, and Z.-H. Zhou. Enhanced (PC)²A for face recognition with one training image per person. Pattern Recognition Letters, 2004, 25(10): 1173-1181. [data]

J. Wu and Z.-H. Zhou. Efficient face candidates selector for face detection. Pattern Recognition, 2003, 36(5): 1175-1186. [demo]

J. Wu and Z.-H. Zhou. Face recognition with one training image per person. Pattern Recognition Letters, 2002, 23(14): 1711-1719. [data]

F. J. Huang, Z.-H. Zhou, H.-J. Zhang, and T. Chen. Pose invariant face recognition. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition (FG'00), Grenoble, France, 2000, pp.245-250.

[go top]

Computer-Aided Medical Diagnosis

Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, 7(1): 37-42. [code]

Z.-H. Zhou, Y. Jiang, Y.-B. Yang, and S.-F. Chen. Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine, 2002, 24(1): 25-36.

[go top]

Bioinformatics

W. Zhang, T. D. Le, L. Liu, Z.-H. Zhou, and J. Li. Mining heterogeneous causal effects for personalized cancer treatment. Bioinformatics, 2017, 33(15): 2372-2378.

W. Zhang, T. D. Le, L. Liu, Z.-H. Zhou, and J. Li. Predicting miRNA targets by integrating gene regulatory knowledge with expression profiles. PLOS One, 2016, 11(4): e0152860.

C. He, Y.-X. Li, G. Zhang, Z. Gu, R. Yang, J. Li, Z. J. Lu, Z.-H. Zhou, C. Zhang, and J. Wang. MiRmat: Mature microRNA sequence prediction. PLOS One, 2012, 7(12): e51673.

S. Ji, L. Yuan, Y.-X. Li, Z.-H. Zhou, S. Kumar, and J. Ye. Drosophila gene expression pattern annotation using sparse features and term-term interactions. In: Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'09), Paris, France, 2009, pp.407-416.

S. Ji, Y.-X. Li, Z.-H. Zhou, S. Kumar, J. Ye. A bag-of-words approach for drosophila gene expression pattern annotation. BMC Bioinformatics, 2009, 10: 119.

[go top]

Software Mining

X. Huo M. Li, and Z.-H. Zhou. Learning unified features from natural and programming languages for locating buggy source codes. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, pp.1606-1612.

C. Qian, Y. Yu, and Z.-H. Zhou. Collisions are helpful for computing unique input-output sequences. In: Poster Proceedings of the ACM 2011 Genetic and Evolutionary Computation Conference (GECCO'11), Dublin, Ireland, 2011, pp.265-266.

M. Li, H. Zhang, R. Wu, and Z.-H. Zhou. Sample-based software defect prediction with active and semi-supervised learning. Automated Software Engineering, 2012, 19(2): 201-230.

Y. Jiang, M. Li, and Z.-H. Zhou. Software defect detection with ROCUS. Journal of Computer Science and Technology, 2011, 26(2): 328-342.

Y. Jiang, M. Li, and Z.-H. Zhou. Mining extremely small data sets with application to software reuse. Software: Practice and Experience, 2009, 39(4): 423-440.

[go top]

Theoretical Aspects of Evolutionary Computation

C. Qian, J.-C. Shi, K. Tang, and Z.-H. Zhou. Constrained monotone k-submodular function maximization using multi-objective evolutionary algorithms with theoretical guarantee. IEEE Transactions on Evolutionary Computation, in press. [code]

C. Qian, Y. Yu, K. Tang, Y. Jin, X. Yao, and Z.-H. Zhou. On the effectiveness of sampling for evolutionary optimization in noisy environments. Evolutionary Computation, in press.

C. Qian, J.-C. Shi, Y. Yu, K. Tang, and Z.-H. Zhou. Subset selection under noise. In: Advances in Neural Information Processing Systems 30 (NIPS'17) (Long Beach, CA), 2017, pp.3563-3573. [code]

C. Qian, J.-C. Shi, Y. Yu, K. Tang, and Z.-H. Zhou. Optimizing ratio of monotone set functions. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.2606-2612. [code]

C. Qian, Y. Yu, and Z.-H. Zhou. A lower bound analysis of population-based evolutionary algorithms for pseudo-Boolean functions. In: Proceedings of the 17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'16), Yangzhou, China, 2016, pp.457-467. (CORR abs/1606.03326) This paper won the Best Paper Award at IDEAL'16

C. Qian, K. Tang, and Z.-H. Zhou. Selection hyper-heuristics can provably be helpful in evolutionary multi-objective optimization. In: Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN'16), Edingburgh, Scotland, 2016, pp.835-846.

C. Qian, J.-C. Shi, Y. Yu, K. Tang, and Z.-H. Zhou. Parallel pareto optimization for subset selection. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, pp.1939-1945. [code]

C. Qian, Y. Yu, and Z.-H. Zhou. Analyzing evolutionary optimization in noisy environments. Evolutionary Computation, in press. (CORR abs/1311.4987)

C. Qian, Y. Yu, and Z.-H. Zhou. Subset selection by pareto optimization. In: Advances in Neural Information Processing Systems 28 (NIPS'15) (Montreal, Canada), C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett, eds. Cambridge, MA: MIT Press, 2015, pp.1765-1773. [code]

Y. Yu, C. Qian, and Z.-H. Zhou. Switch analysis for running time analysis of evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 2015, 15(6): 777-792.

C. Qian, Y. Yu, and Z.-H. Zhou. Pareto ensemble pruning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), Austin, TX, 2015, pp. 2935-2941. [code]

C. Qian, Y. Yu, and Z.-H. Zhou. Variable solution structure can be helpful in evolutionary optimization. Science China Information Sciences, 2015, 58(11): 112105.

C. Qian, Y. Yu, Y. Jin, and Z.-H. Zhou. On the effectiveness of sampling for evolutionary optimization in noisy environments. In: Proceedings of the 13th International Conference on Parallel Problem Solving from Nature (PPSN'14), Ljubljana, Slovenia, LNCS 8672, 2014, pp.302-311.

C. Qian, Y. Yu, and Z.-H. Zhou. An analysis on recombination in multi-objective evolutionary optimization. Artificial Intelligence, 2013, 204: 99-119.

C. Qian, Y. Yu, and Z.-H. Zhou. On algorithm-dependent boundary case identification for problem classes. In: Proceedings of the 12th International Conference on Parallel Problem Solving from Nature (PPSN'12), Taormina, Italy, LNCS 7491, 2012, pp.62-71.

Y. Yu, X. Yao, and Z.-H. Zhou. On the approximation ability of evolutionary optimization with application to minimum set cover. Artificial Intelligence, 2012, 180-181: 20-33. (CORR abs/1011.4028)

Y. Yu, C. Qian, and Z.-H. Zhou. Towards analyzing crossover operators in evolutionary search via general Markov chain switching theorem. CORR abs/1111.0907, 2011.

C. Qian, Y. Yu, and Z.-H. Zhou. An analysis on recombination in multi-objective evolutionary optimization. In: Proceedings of the 13th ACM Genetic and Evolutionary Computation Conference (GECCO'11), Dublin, Ireland, 2011, pp.2051-2058. This paper won the Theory Best Paper Award at GECCO'11

Y. Yu, C. Qian, and Z.-H. Zhou. Towards analyzing recombination operators in evolutionary search. In: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN'10), LNCS 6238, Krakow, Poland, 2010, pp.144-153.

Y. Yu and Z.-H. Zhou. On the usefulness of infeasible solutions in evolutionary search: A theoretical study. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC'08), Hong Kong, China, 2008, pp.835-840.

Y. Yu and Z.-H. Zhou. A new approach to estimating the expected first hitting time of evolutionary algorithms. Artificial Intelligence, 2008, 172(15): 1809-1832.

Y. Yu and Z.-H. Zhou. A new approach to estimating the expected first hitting time of evolutionary algorithms. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06), Boston, MA, 2006, pp.555-560.

[go top]

Improving Comprehensibility

Y. Jiang, M. Li, and Z.-H. Zhou. Mining extremely small data sets with application to software reuse. Software: Practice and Experience, 2009, 39(4): 423-440.

Z.-H. Zhou. Comprehensibility of data mining algorithms. In: J. Wang ed. Encyclopedia of Data Warehousing and Mining, Hershey, PA: IGI, 2005, 190-195.

Z.-H. Zhou and Y. Jiang. NeC4.5: Neural ensemble based C4.5. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(6): 770-773. [code]

Z.-H. Zhou. Rule extraction: Using neural networks or for neural networks? Journal of Computer Science and Technology, 2004, 19(2): 249-253.

Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, 7(1): 37-42. [code]

Z.-H. Zhou, Y. Jiang, and S.-F. Chen. Extracting symbolic rules from trained neural network ensembles. AI Communications, 2003, 16(1): 3-15.

Z.-H. Zhou and Z.-Q. Chen. Hybrid decision tree. Knowledge-Based Systems, 2002, 15(8): 515-528.

[go top]

Miscellaneous

W. Gao and Z.-H. Zhou. Dropout Rademacher complexity of deep neural networks. Science China Information Sciences, 2016, 59(7): 072104:1-072104:12. (CORR abs/1402.3811)

J. T. Kwok, Z.-H. Zhou, and L. Xu. Machine learning. In: J. Kacprzyk, W. Pedrycz, eds. Springer Handbook of Computational Intelligence, Berlin: Springer, 2015, 495-522.

Z.-H. Zhou, N. V. Chawla, Y. Jin, and G. J. Williams. Big data opportunities and challenges: Discussions from data analytics perspectives. IEEE Computational Intelligence Magazine, 2014, 9(4): 62-74. This article won the 2017 IEEE CIS Outstanding Computational Intelligence Magazine Paper Award

Z.-H. Zhou. Three perspectives of data mining. Artificial Intelligence, 2003, 143(1): 139-146.

Z.-H. Zhou and S.-F. Chen. Evolving fault-tolerant neural networks. Neural Computing & Applications, 2003, 11(3-4): 156-160.

Z.-H. Zhou. Review on Data Mining: Concepts and Techniques. IEEE Transactions on Neural Networks, 2002, 13(5): 1251.

Z.-H. Zhou, S.-F. Chen, and Z.-Q. Chen. Improving tolerance of neural networks against multi-node open fault. In: Proceedings of the INNS-IEEE International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, 2001, vol.3, pp.1687-1692.

Z.-H. Zhou, S. Chen, and Z. Chen. FANNC: A fast adaptive neural network classifier. Knowledge and Information Systems, 2000, 2(1): 115-129. [demo]

[go top]

[Home]