<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/17365" mdate="2013-10-02 00:00:00"> <author>Mohamed EL Boujnouni and Mohamed Jedra and Noureddine Zahid</author> <title>Kernel&amp;rsquo;s Parameter Selection for Support Vector Domain Description</title> <pages>1165 - 1170</pages> <year>2013</year> <volume>7</volume> <number>8</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/17365.pdf</ee> <url>https://publications.waset.org/vol/80</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Support Vector Domain Description (SVDD) is one of the bestknown oneclass support vector learning methods, in which one tries the strategy of using balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. As all kernelbased learning algorithms its performance depends heavily on the proper choice of the kernel parameter. This paper proposes a new approach to select kernel&amp;amp;39;s parameter based on maximizing the distance between both gravity centers of normal and abnormal classes, and at the same time minimizing the variance within each class. The performance of the proposed algorithm is evaluated on several benchmarks. The experimental results demonstrate the feasibility and the effectiveness of the presented method. </abstract> <index>Open Science Index 80, 2013</index> </article>