A Fast Incremental Learning Algorithm Based on Twin Support Vector Machine

Abstract

Twin support vector machine is a novel classifier, it construct two nonparallel hyper planes instead of a single hyper plane to obtain four times faster than the usual SVM. With the result of traditional incremental learning method of SVM, we analyze the characteristics of twin support vector machine and the distribution of the training sample set. In this paper, we propose a fast incremental learning algorithm based on twin support vector machine. It can deal with the newly added training samples and utilize the result of the previous training effectively. Experimental results prove that the given algorithm has excellent classification performance on runtime and recognition rate, and therefore confirm the above conclusion further.