Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters
نویسندگان
چکیده
منابع مشابه
Sparse least squares Support Vector Machine classifiers
In least squares support vector machine (LS-SVM) classi-ers the original SVM formulation of Vapnik is modiied by considering equalit y constraints within a form of ridge regression instead of inequality constraints. As a result the solution follows from solving a set of linear equations instead of a quadratic programming problem. Ho wever, a d r a wback is that sparseness is lost in the LS-SVM ...
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In least squares support vector machine (LS-SVM) classi-ers the original SVM formulation of Vapnik is modiied by considering equality constraints within a form of ridge regression instead of inequality constraints. As a result the solution follows from solving a set of linear equations instead of a quadratic programming problem. However, a drawback is that sparseness is lost in the LS-SVM case ...
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ژورنال
عنوان ژورنال: International Journal of Computational Intelligence Systems
سال: 2020
ISSN: 1875-6883
DOI: 10.2991/ijcis.d.200205.001