Two-stage fast training method based on core vector machine and support vector machine

Abstract

Support Vector Machine(SVM) is a widely used classification technique.But the scalability of SVM to handle large data sets still needs much of exploration.Core Vector Machine(CVM) is a technique for scaling up a two class SVM to handle large data sets.However,it is computationally infeasible to use CVM to deal with the data set with mass Support Vectors(SV),as its training time is related to the number of SV.In this paper,a two-stage training algorithm combining CVM with SVM(CCS) was proposed.It first employed Minimum Enclosing Ball(MEB) based CVM algorithm to determine the potential core vectors,and then used labeling method to rapidly reconstruct training set,which aim is to reduce the scale of training set.After obtaining new training samples,SVM was adopted to deal with them.The experimental results indicate that the proposed approach can reduce the training time by 30% without losing the classification accuracy,and it is an efficient method for handling large-scale classification.