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A Novel Kernel for Least Squares Support Vector Machine

A Novel Kernel for Least Squares Support Vector Machine
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摘要 Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms. Extreme learning machine（ELM） has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression（LSSVR）,so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms.
作者 冯伟 赵永平 杜忠华 李德才 王立峰
机构地区 School of Mechanical Engineering Xi'an Modern Chemistry Research Institute Heilongjiang North Tool Co.Ltd.
出处 《Defence Technology（防务技术）》 SCIE EI CAS 2012年第4期240-247,共8页 Defence Technology
基金 Sponsored by the National Natural Science Foundation of China(51006052)
关键词 计算技术 理论 方法 自动机理论 artificial intelligence extreme learning machine support vector machine kernel method
分类号 TP18 [自动化与计算机技术—控制理论与控制工程] O241.5 [理学—计算数学]
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Defence Technology（防务技术）
2012年 第4期

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