Multi-class SVM based on SOM decoding
Tao Xiao-yan · Systems engineering and electronics · 2006
A multi-class SVM algorithm based on SOM decoding is presented.First,the binary SVM classifiers are trained according to the error correcting output codes(ECOC).Then the SOM network is trained with the output of the training samples and the optimum weights are obtained.Finally the unknown data is classified.By this method,the confidence of the binary classifiers is completely considered with the case avoided that the same minimum distance to several classes is obtained.The experimental results on the Iris data set and Yale face database show that the new algorithm is feasible for the multi-class SVM.