A Novel Kernel for Least Squares Support Vector Machine

被引:0
|
作者
冯伟 [1 ,2 ]
赵永平 [1 ]
杜忠华 [1 ]
李德才 [3 ]
王立峰 [3 ]
机构
[1] School of Mechanical Engineering,Nanjing University of Science and Technology
[2] Xi'an Modern Chemistry Research Institute
[3] Heilongjiang North Tool Co.Ltd.
基金
中国国家自然科学基金;
关键词
artificial intelligence; extreme learning machine; support vector machine; kernel method;
D O I
暂无
中图分类号
TP18 [人工智能理论]; O241.5 [数值逼近];
学科分类号
070102 ; 081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码:240 / 247
页数:8
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