v-Support vector machine with weight of samples

doi:10.3969/j.issn.1000-1565.2018.02.011

Abstract

Abstract: The v-support vector machine(v-SVM)overcomes the difficult problem with selection of parameter for the traditional support vector machine.However,this method does not take into account the effect of different samples in the process of training,which leads to a strong sensitivity to noise or outlier points so that training is easy to be over-fit.In order to solve this problem,an improved v-SVM model is obtained by introducing the weight of the samples for the effect of different samples.A Lagrange method is used to solve the model,and a support vector machine classifier is obtained.To show the effectiveness of the presented method,the experiments are conducted using at two kinds of methods for determining weights of samples on 10 standard data sets from UCI repository,and compared with the performance of C-SVM and v-SVM.

Key words: support vector machine, v-fuzzy support vector machine, weight, fuzzy clustering, kernel track

CLC Number:

TP319

LI Kai,ZHAI Lulu,CUI Lijuan. v-Support vector machine with weight of samples[J]. Journal of Hebei University (Natural Science Edition), 2018, 38(2): 185-193.

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