A NOVEL REDUCED SUPPORT VECTOR MACHINE ON MORLET WAVELET KERNEL FUNCTION

摘要

A novel reduced support vector machine on Morlet wavelet kernel function (MWRSVM-DC) is proposed, which combined the Morlet wavelet kernel function with the reduced support vector machine (SVM) model properly. Based on the wavelet decomposition and conditions of the support vector kernel function, Morlet wavelet kernel function for support vector machine (SVM) is proposed. At the same time, because SVM is restricted to work well on the small sample sets, a novel reduced SVM on density clustering (RSVM-DC) is proposed. This algorithm focuses on dealing with a sample set through density clustering prior to classifying the samples. After clustering the positive samples and negative samples, the algorithm picks out such samples that locate on the edge of clusters as reduced samples. These reduced samples are treated as the new training sample set used in SVM's classifier system. Experiment results show that not only the precision but also the efficiency of SVM's are improved by MWRSVM-DC.