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《Journal of Hubei University for Nationalities(Natural Science Edition)》 2018-01
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Iterative Algorithm for Separating Hyperplane in Support Vector Machines

YI Xiaoshi;LIU Nian;School of Mathematical Sciences,Chongqing Normal University;College of Mathematics and Statistics,Chongqing University;  
Support vector machine(SVM) is a planned learning algorithm,the key point of which is to obtain the separating hyperplane,and turn the question into convex quadratic programming.This paper solves the separating hyperplane of support vector machine by perceptron iterative algorithm.The solution is divided into two phases.First,the iterative algorithm of perceptron is utilized to obtain an initial separating hyperplane.Second,the initial hyperplane should be rotated and moved continuously until the distance between positive support vector and the separating hyperplane is equal to that between negative support vector and the separating hyperplane.Then the separating hyperplane of SVM can be obtained.The end of paper explains a test that is made on Iris L.,classified data by using the iterative algorithm and convex quadratic programming.The result is completely consistent with that of the support vectors.Therefore,it is proven that the iterative algorithm in this paper is not only concise but also effective.
【Fund】: 国家自然科学基金项目(11371384)
【CateGory Index】: TP181
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