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Linear programming support vector machines
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DOI：10.1016/S0031-3203(01)00210-2.png)
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Abstract
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Based on the analysis of the conclusions in the statistical learning theory, especially the VC dimension of linear functions linear programming support vector machines (or SVMs) are presented including linear programming linear and nonlinear SVMs. In linear programming SVMs, in order to improve the speed of the training time, the bound of the VC dimension is loosened properly. Simulation results for both artificial and real data show that the generalization performance of our method is a good approximation of SVMs and the computation complex is largely reduced by our method. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
statistical learning theory
VC dimension
support vector machines
generalization performance
linear programming
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