Int. J. of Granular Computing, Rough Sets and Intelligent Systems   »   2010 Vol.1, No.3

 

 

Title: Adaptive and iterative least squares support vector regression based on quadratic Renyi entropy

 

Author: Jingqing Jiang, Chuyi Song, Haiyan Zhao, Chunguo Wu, Yanchun Liang

 

Addresses:
College of Mathematics and Computer Science, Inner Mongolia University for Nationalities, Tongliao Inner Mongolia 028043, China.
College of Mathematics and Computer Science, Inner Mongolia University for Nationalities, Tongliao Inner Mongolia 028043, China.
College of Mathematics and Computer Science, Inner Mongolia University for Nationalities, Tongliao Inner Mongolia 028043, China.
College of Computer Science and Technology, Jilin University, Changchun 130012, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.
College of Computer Science and Technology, Jilin University, Changchun 130012, China

 

Abstract: An adaptive and iterative LSSVR algorithm based on quadratic Renyi entropy is presented in this paper. LS-SVM loses the sparseness of support vector which is one of the important advantages of conventional SVM. The proposed algorithm overcomes this drawback. The quadratic Renyi entropy is the evaluating criterion for working set selection, and the size of working set is determined at the process of iteration adaptively. The regression parameters are calculated by incremental learning and the calculation of inversing a large scale matrix is avoided. So the running speed is improved. This algorithm reserves well the sparseness of support vector and improves the learning speed.

 

Keywords: least squares support vector regression; quadratic Renyi entropy; incremental learning; adaptive LSSVR; iterative LSSVR; learning speed.

 

DOI: 10.1504/IJGCRSIS.2010.029579

 

Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2010 Vol.1, No.3, pp.221 - 232

 

Available online: 30 Nov 2009

 

 

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