Optimal reduction of solutions for support vector machines

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Title:	Optimal reduction of solutions for support vector machines
Authors:	Lin, Hwei-Jen;Yeh, Jih-Pin
Contributors:	淡江大學資訊工程學系
Keywords:	Support vector machine;Vector correlation;Genetic algorithms;Optimal solution;Discriminant function;Pattern recognition
Date:	2009-08
Issue Date:	2011-05-20 09:58:43 (UTC+8)
Publisher:	Philadelphia: Elsevier Inc.
Abstract:	Being a universal learning machine, a support vector machine (SVM) suffers from expensive computational cost in the test phase due to the large number of support vectors, and greatly impacts its practical use. To address this problem, we proposed an adaptive genetic algorithm to optimally reduce the solutions for an SVM by selecting vectors from the trained support vector solutions, such that the selected vectors best approximate the original discriminant function. Our method can be applied to SVMs using any general kernel. The size of the reduced set can be used adaptively based on the requirement of the tasks. As such the generalization/complexity trade-off can be controlled directly. The lower bound of the number of selected vectors required to recover the original discriminant function can also be determined.
Relation:	Applied Mathematics and Computation 214(2), pp.329-335
DOI:	10.1016/j.amc.2009.04.010
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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