Multi-class SVMs based on SOM decoding algorithm and its application in pattern recognition

Authors:
Xiaoyan Tao;Hongbing Ji
Affiliations:
School of Electronic Engineering, Xidian University, Xi’an, China;School of Electronic Engineering, Xidian University, Xi’an, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

Recently, multi-class SVMs have attracted much attention due to immense demands in real applications. Both the encoding and decoding strategies critically influence the effectiveness of the multi-class SVMs. In this work, a multi-class SVMs based on the SOM decoding algorithm is proposed. First, the binary SVM classifiers are trained according to the ECOC codes. 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 Yale face database demonstrate the superiority of the new algorithm over the widely-used Hamming decoding method.