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The Inverse Problem Of Support Vector Machines And Its Solution

Posted on:2006-07-22Degree:MasterType:Thesis
Country:ChinaCandidate:W K TengFull Text:PDF
GTID:2168360155450316Subject:Basic mathematics
Abstract/Summary:PDF Full Text Request
Support vector machine (SVM) is novel type learning machine, based on statistical learning theory, which tasks involving classification, regression or novelty detection. 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 maximum. Here the margin is defined according to the separate hyper-plan and support vectors of SVMs. It is difficult to give 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. The inverse problem of SVMs can be considered as a heuristic to generate decision trees with high generalization capability.
Keywords/Search Tags:statistical learning theory, support vector machine, hyperplane, genetic algorithm
PDF Full Text Request
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