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- W2364409237 abstract "Support vector machine(SVM) is a kind of novel learning method,which is based on structural risk minimization principle,unlike traditional machine learning which is based on empirical risk minimization principle.SVM has shown powerful ability in learning with limited samples.But in SVM,the solution of the problem is characterized by a convex quadratic programming problem.In least squares SVM(LS-SVM),a least squares cost function is proposed so as to obtain a linear set of equations in dual space.Through genetic algorithm,parameters optimization can be solved.The model of LS-SVM is then used to forecast a nonlinear macroeconomic system.It is shown that the LS-SVM based on parameters optimization by genetic algorithm is simple and effective." @default.
- W2364409237 created "2016-06-24" @default.
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- W2364409237 date "2009-01-01" @default.
- W2364409237 modified "2023-09-25" @default.
- W2364409237 title "Method for selecting parameters of least squares support vector machines and application" @default.
- W2364409237 hasPublicationYear "2009" @default.
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