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OLTV

Description: OLTV is a package for learning with only one labeled training example along with abundant unlabeled training instances, given that the data has two views, i.e. there are two attribute subsets each of which is sufficient for building a good classifier.

References:  

• 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.

ATTN:        This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (zhouzh@nju.edu.cn).

Requirement: The package was developed with MATLAB and WEKA. The Java routines can be called in MATLAB environment.

ATTN2:       This package was developed by Mr. De-Chuan Zhan (zhandc@lamda.nju.edu.cn). For any problem concerning the code, please feel free to contact Mr. Zhan.

Download:   [code] (18Kb)