Weighted least squares support vector machines: robustness and sparse approximation
Abstract
null null Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem. However, sparseness is lost in the LS-SVM case and the estimation of the support values is only optimal in the case of a Gaussian distribution of the...
Paper Details
Title
Weighted least squares support vector machines: robustness and sparse approximation
Published Date
Oct 1, 2002
Journal
Volume
48
Issue
1
Pages
85 - 105
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