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Reseach Article

Training a Support Vector Classifier using a Cauchy-Laplace Product Kernel

by P.Chandrasekhar, B.Mallikarjuna Reddy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 21
Year of Publication: 2015
Authors: P.Chandrasekhar, B.Mallikarjuna Reddy
10.5120/20474-6205

P.Chandrasekhar, B.Mallikarjuna Reddy . Training a Support Vector Classifier using a Cauchy-Laplace Product Kernel. International Journal of Computer Applications. 116, 21 ( April 2015), 48-52. DOI=10.5120/20474-6205

@article{ 10.5120/20474-6205,
author = { P.Chandrasekhar, B.Mallikarjuna Reddy },
title = { Training a Support Vector Classifier using a Cauchy-Laplace Product Kernel },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 21 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 48-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number21/20474-6205/ },
doi = { 10.5120/20474-6205 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:57:49.316820+05:30
%A P.Chandrasekhar
%A B.Mallikarjuna Reddy
%T Training a Support Vector Classifier using a Cauchy-Laplace Product Kernel
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 21
%P 48-52
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The importance of the support vector machine and its applicability to a wide range of problems is well known. The strength of the support vector machine lies in its kernel. In our recent paper, we have shown how the Laplacian kernel overcomes some of the drawbacks of the Gaussian kernel. However this was not a total remedy for the shortcomings of the Gaussian kernel. In this paper, we design a Cauchy-Laplace product kernel to further improve the performance of the Laplacian kernel. The new kernel alleviates the deficiencies more effectively. During the experimentation with three data sets, it is found that the product kernel not only enhances the performance of the support vector machine in terms of classification accuracy but it results in obtaining higher classification accuracy for smaller values of the kernel parameter ?. Therefore the support vector machine gives smoother decision boundary and the results obtained by the product kernel are more reliable as it overcomes the problems of over fitting.

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Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine Hyper Plane Mercer Kernel Laplacian Kernel Cauchy-Laplace Product Kernel