// trainset_filename		File name of the training set
C:\WuJx\MITFaces\datasetMike.dat
// validset_filename		File name of the validation set C:\WuJx\MITFaces\ProfileValid.dat 
C:\WuJx\MITFaces\datasetTrainValid.dat
// classifier_filename		File name of the file which stored all features (weak classifeirs)
classifiers.txt
// ada_log_filename		File name to save the log file produced by the Viola-Jones algorithm
rate_adaboost.txt
// cascade_filename		File name to save the trained cascade classifier
cascade.txt
// FFS_WeakClassifiers_filename	File used to save the trained weak classifiers by the FFS algorithm
FFS_Weak.txt
// FFS_log_filename		File name to save the log file produced by the FFS algorithm
rate_FFS.txt
// FileUsage_log_filename	File to record the usage information of the bootstrapping dataset
fileused.txt
// Bootstrap_database_filename	File to list all files in the bootstrapping dataset
C:\WuJx\SkinColor\non-skin-images\0_FileList.txt
// Backup_directory_name	Directory to save all the backup information during the training process
C:\WuJx\MITFaces\History46\
// sx				Width of input training image
24
// sy				Height of input training image
24
// train_method			Which method is used to train?
1
// bootstrap_level		The current bootstrap level
0
// max_bootstrap_level		Maximum bootstrap level allowed
2
// bootstrap_resizeratio	Array of resize ratio in bootstrapping for differnt levels
0.5 0.8
// bootstrap_increment		Array of step size in bootstrapping for differnt levels   
  3  1
// totalfeatures		Number of features of the feature set used
16233
// max_files			Maximum number of files in the bootstrapping image database
30000
// goal_method			Which method is used to determine ensemble threshold
3
// node_det_goal		Detection rate goal for a cascade node
0.95
// node_fp_goal			False positive rate goal for a cascade node
0.5
// first_feature		In FFS, which feature is the first to be choosed?
0
// asym_ratio			Level of asymmetry in Asymetric AdaBoost
1.0268
// max_nodes			How many nodes are allowed?
50
// nof				How many features in each node?
020 040 060 080 100 120 140 160 180 200
200 200 200 200 200 200 200 200 200 200
200 200 200 200 200 200 200 200 200 200
200 200 200 200 200 200 200 200 200 200
200 200 200 200 200 200 200 200 200 200
// starting_node		Resume Training from which node?
1