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发表时间：2015-01-01

发表刊物：控制与决策

所属单位：电子信息与电气工程学部

期号：9

页面范围：1603-1608

ISSN号：1001-0920

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.

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