Training SVMs for Multiple Features Classification Problems

Abstract

A novel method is presented in this paper to study the use of SVM classifiers for multiple feature classification. While commonly multiple binary SVM classifiers are trained on features individually and the outputs of the classifiers are linearly combined for multiple feature classification, our method trains and combines these classifiers simultaneously with lower complexity. To obtain the optimal/suboptimal weights of different classifiers, an efficient algorithm is developed to takes into account both a base classifier's performance on the training data and its generalization ability, while traditional combination approaches consider a base classifierpsilas performance only. Experiments were performed and the results demonstrate the effectiveness and efficiency of the novel approach.