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Sparse approximation using least squares support vector machines
In least squares support vector machines (LS-SVMs) for function estimation Vapnik's /spl epsiv/-insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression. In this way nonlinear function estimation is done by solving a linear set of equations...
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Main Authors: | , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | In least squares support vector machines (LS-SVMs) for function estimation Vapnik's /spl epsiv/-insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression. In this way nonlinear function estimation is done by solving a linear set of equations instead of solving a quadratic programming problem. The LS-SVM formulation also involves less tuning parameters. However, a drawback is that sparseness is lost in the LS-SVM case. In this paper we investigate imposing sparseness by pruning support values from the sorted support value spectrum which results from the solution to the linear system. |
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DOI: | 10.1109/ISCAS.2000.856439 |