Paper Title:
Colonial Competitive Algorithm Assisted Least Squares Support Vector Machines
  Abstract

The use of least squares support vector machine (LSSVM), a novel machine learning method, for classification and function approximation has increased over the past few years especially due to its high generalization performance. However, LSSVM is plagued by the drawback that the hyper-parameters, which largely determine the quality of LSSVM models, have to be defined by the user, and this increases the difficulty of applying LSSVM and limits its use on academic and industrial platforms. In this paper we present a novel method of automatically tuning the hyper-parameters of LSSVM based on colonial competitive algorithm (CCA), a newly developed evolutionary algorithm inspired by imperialistic competition mechanism. To show the efficacy of the CCA assisted LSSVM methodology, we have tested it on several benchmark examples. The study suggests that proposed paradigm can be a competitive and powerful tool for classification and function approximation.

  Info
Periodical
Advanced Materials Research (Volumes 255-260)
Edited by
Jingying Zhao
Pages
2082-2086
DOI
10.4028/www.scientific.net/AMR.255-260.2082
Citation
Y. Ren, G. C. Bai, "Colonial Competitive Algorithm Assisted Least Squares Support Vector Machines", Advanced Materials Research, Vols. 255-260, pp. 2082-2086, 2011
Online since
May 2011
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Price
$32.00
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