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IIJCS:Volume 5, Issue 8, August 2017


Comparison of performance with support vector machines
Author Name:
Kai Li, Lulu Zhai
ABSTRACT Support vector machine (SVM) is an important machine learning method, which has many applications in pattern recognition, network security, etc. However, this method has some shortcomings such as complicated computation of quadratic programming, time-consuming training and low anti-noise performance. To this end, the researchers have proposed some improved methods. In this paper, we select the commonly used SVM classifiers including C-SVM, v-SVM, PSVM and TWSVM to study their performance of classification on the standard data set by experiments. The experimental results are shown that the performance with TWSVM has an advantage over C-SVM, v-SVM and PSVM in selected ten data sets using linear kernel. However, when Gauss kernel is used, accuracy with different support vector machine is almost no differences except data set wdbc. Keywords: support vector machine, proximal support vector machine, twin support vector machine, kernel function, classification
Cite this article:
Kai Li, Lulu Zhai , " Comparison of performance with support vector machines" , IPASJ INTERNATIONAL JOURNAL OF COMPUTER SCIENCE(IIJCS) , Volume 5, Issue 8, August 2017 , pp. 024-032 , ISSN 2321-5992.
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