Journal
NEUROCOMPUTING
Volume 68, Issue -, Pages 225-238Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2005.05.006
Keywords
support vector machines; genetic algorithms; maximum margin
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This paper investigates an inverse problem of support vector machines (SVMs). The inverse problem is how to split a given dataset into two clusters such that the margin between the two clusters attains the maximum. Here the margin is defined according to the separating hyper-plane generated by support vectors. It is difficult to give an exact solution to this problem. In this paper, we design a genetic algorithm to solve this problem. Numerical simulations show the feasibility and effectiveness of this algorithm. This study on the inverse problem of SVMs is motivated by designing a heuristic algorithm for generating decision trees with high generalization capability. (c) 2005 Elsevier B.V. All rights reserved.
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