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.
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
.
DOI: 10.3969/j.issn.1000-1565.2018.02.011
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