Multiple kernel learning LSSVR algorithm based on lp-norm constraint

被引：0

作者：

The Liaoning Key Lab of Advanced Control Systems for Industry Equipments,, Dalian University of Technology,, Dalian ^{[1
]}

116024, China

机构：

[1] The Liaoning Key Lab of Advanced Control Systems for Industry Equipments,, Dalian University of Technology,, Dalian

来源：

Kongzhi yu Juece Control Decis | / 9卷 / 1603-1608期

关键词：

Generalization performance; l[!sub]p[!/sub]-norm constraint; Least squares support vector machine; Multiple kernel learning;

D O I：

10.13195/j.kzyjc.2014.0867

中图分类号：

学科分类号：

摘要：

In order to improve generalization performance of learning least squares support vector machines regression(LSSVR), a novel multiple kernel learning least squares support vector machines regression algorithm based on lp-Norm constraint is proposed. Two wrapper methods are provided to solve the proposed algorithm, and both the training method are two-step methods. The inner loop is used to update the combination function parameters while fixing the least squares support vector machine (LSSVM) parameters, the outside loop is used to update the parameters of LSSVM while fixing the combination function parameters, and these two steps are repeated until convergence. The simulation on the one-variable function and multivariable function shows that the proposed algorithm is useful and outperforms the traditional LSSVR algorithm for generalization performance. ©, 2015, Northeast University. All right reserved.

引用

页码：1603 / 1608

页数：5

共 13 条

[11] Zhang X., Hu L., Zhang L., An efficient multiple kernel computation method for regression analysis of economic data, Neurocomputing, 118, 10, pp. 58-64, (2013)
[12] Ceperic V., Gielen G., Baric A., Sparse multikernel support vector regression machines trained by active learning, Expert Systems with Applications, 39, 12, pp. 11029-11035, (2012)
[13] Cai Y., Wang H., Ye X., Et al., A multiple-kernel LSSVR method for separable nonlinear system identification, J of Control Theory and Applications, 11, 4, pp. 651-655, (2013)

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