A gradual combining method for multi-SVM classifiers based on distance estimation

A fusion algorithm based on multi SVM classifiers is presented in order to improve the performance of SVMs (support vector machines). Different SVM classifiers are trained with special instances. A gradual method based on distance estimation is utilized to combine different SVM classifiers into a sole learner. Instances that are easy to be categorized mistakenly by present classifier will be handed to the next classifier. These instances are chosen according to their distance to the optimal discrimination hyperplane. Evaluation on efficacy of the proposed multi-SVM classifier is carried on Chinese personal name recognition. Experiments show this multi SVM classifiers achieve better performance than that of single SVM learner and SVM ensemble using weighted voting scheme.