On the Sparseness and Generalization Capability of Least Squares Support Vector Machines

Yan Aijun (), Huang Xiaoqian () and Shao Hongshan ()
Additional contact information
Yan Aijun: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing100124, China
Huang Xiaoqian: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing100124, China
Shao Hongshan: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing100124, China

Journal of Systems Science and Information, 2015, vol. 3, issue 3, 279-288

Abstract: Compared with standard support vector machines (SVM), sparseness is lost in the modeling process of least squares support vector machines (LS-SVM), causing limited generalization capability. An improved method using quadratic renyi-entropy pruning is presented to deal with the above problems. First, a kernel principal component analysis (KPCA) is used to denoise the training data. Next, the authors use the genetic algorithm to estimate and optimize the kernel function parameter and penalty factor. Then, pick the subset that has the largest quadratic entropy to train and prune, and repeat this process until the cumulative error rate reaches the condition requirement. Finally, comparing experiments on the data classification and regression indicates that the proposed method is effective and may improve the sparseness and the generalization capability of LS-SVM model.

Keywords: LS-SVM; sparseness; generalization capability; quadratic renyi-entropy pruning (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/JSSI-2015-0279 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:3:y:2015:i:3:p:279-288:n:6

DOI: 10.1515/JSSI-2015-0279

Access Statistics for this article

Journal of Systems Science and Information is currently edited by Shouyang Wang

More articles in Journal of Systems Science and Information from De Gruyter
Bibliographic data for series maintained by Peter Golla ().