An Improved LSSVM Regression Algorithm

Author

Hou, Likun ; Yang, Qingxin ; An, Jinlong

Author_Institution

Province-Minist. Joint Key Lab. of Electromagn. Field & Electr. Apparatus Reliability, Hebei Univ. of Technol., Tianjin, China

Volume

2

fYear

2009

fDate

6-7 June 2009

Firstpage

138

Lastpage

140

Abstract

Support vector machine (SVM) is a new and valid machine-learning algorithm developed on statistical learning theory, and it has been used for classification, function regression, and time series prediction. Recently an extension of traditional SVM named LSSVM has been introduced. Compared with the support vector machine, the least squares support vector machine (LSSVM) lose the sparseness, which would influence the efficiency of relearning. To conclude a sparse solution, in this paper we present an improved algorithm for least squares support vector machine - XS-LSSVM, and prove its effect by an simulation experiment.

Keywords

least squares approximations; regression analysis; support vector machines; time series; LSSVM regression algorithm; function regression; least squares support vector machine; machine-learning algorithm; statistical learning theory; time series; Computational intelligence; Electromagnetic fields; Lagrangian functions; Least squares approximation; Least squares methods; Multidimensional systems; Reliability theory; Statistical learning; Support vector machine classification; Support vector machines; LSSVM; SVM; SVM regression; modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3645-3

Type

conf

DOI

10.1109/CINC.2009.247

Filename

5231009