Improved Scheme for Fast Approximation to Least Squares Support Vector Regression

被引：0

作者：

张宇宸 ^{[1
]}

赵永平 ^{[1
]}

宋成俊 ^{[2
]}

侯宽新 ^{[3
]}

脱金奎 ^{[4
]}

叶小军 ^{[5
]}

机构：

[1] School of Mechanical Engineering,Nanjing University of Science and Technology

来源：

Transactions of Nanjing University of Aeronautics and Astronautics | 2014年 / 31卷 / 04期

基金：

中国国家自然科学基金;

关键词：

support vector regression; kernel method; least squares; sparseness;

D O I：

10.16356/j.1005-1120.2014.04.010

中图分类号：

TP181 [自动推理、机器学习];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FSA-LSSVR,is proposed.Compared with the previously approximate algorithms,it not only adopts the partial reduction strategy but considers the influence between the previously selected support vectors and the willselected support vector during the process of computing the supporting weights.As a result,I2FSA-LSSVR reduces the number of support vectors and enhances the real-time.To confirm the feasibility and effectiveness of the proposed algorithm,experiments on benchmark data sets are conducted,whose results support the presented I2FSA-LSSVR.

引用

页码：413 / 419

页数：7

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