用纠错编码改进的M-ary支持向量机多类分类算法

Enhanced M-ary support vector machine by error correction coding for multi-category classification

针对M-ary支持向量机(SVM)多类分类算法结构简单,但泛化能力较弱的特点,提出了与纠错编码理论相结合的改进的M-ary SVM算法。首先,将原始类别信息编码作为信息码;然后结合纠错编码理论及期望的纠错能力,产生一定程度上性能最佳的编码,作为分类器训练的依据;最后,对于识别阶段输出编码中的错误分类利用检错纠错原理进行校正。实验结果表明,改进的算法通过引入尽可能少的冗余子分类器增强了标准M-ary SVM多类分类算法的性能。

M-ary Support Vector Machine（M-ary SVM） for multi-category classification has the advantage of simple structure,but the disadvantage of weak generalization ability.This paper presented an enhanced M-ary SVM algorithm in combination with error correction coding theory.The main idea of the approach was to generate a group of best codes based on information codes derived from the original category flags information,then utilize such codes as the basis for training the classifier,while in the final feed-forward phase the output codes composed of each sub-classifier could be corrected by error detection and correction principle if there exists any identifying error.The experimental results confirm the effectiveness of the improved algorithm brought about by introducing as few sub-classifiers as possible.