After feature extraction based on Fisher criterion with multicalsses, the projections are discriminated ultilizing the nearest neighbor algorithm and one-versus-one ballot to have a cross validation

FisherMultiClassDiscri
......................\error_validation_multicalss.m
......................\error_validation_oneverseone.m
......................\fisher_multi_class.m
......................\fisher_oneverseone.m
......................\NearestEstimate.m
......................\NearestEstimate_indx.m
......................\orl_faces.mat
......................\sw_multi.mat
......................\train_svm.m
......................\w.mat
......................\w_10class.mat
......................\w_multi.mat
......................\w_oneverse_ALL.mat

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