A Novel Sparse Least Squares Support Vector Machines
Joint Authors
Xia, Xiao-Lei
Jiao, Weidong
Li, Kang
Irwin, George
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-02-07
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
The solution of a Least Squares Support Vector Machine (LS-SVM) suffers from the problem of nonsparseness.
The Forward Least Squares Approximation (FLSA) is a greedy approximation algorithm with a least-squares loss function.
This paper proposes a new Support Vector Machine for which the FLSA is the training algorithm—the Forward Least Squares Approximation SVM (FLSA-SVM).
A major novelty of this new FLSA-SVM is that the number of support vectors is the regularization parameter for tuning the tradeoff between the generalization ability and the training cost.
The FLSA-SVMs can also detect the linear dependencies in vectors of the input Gramian matrix.
These attributes together contribute to its extreme sparseness.
Experiments on benchmark datasets are presented which show that, compared to various SVM algorithms, the FLSA-SVM is extremely compact, while maintaining a competitive generalization ability.
American Psychological Association (APA)
Xia, Xiao-Lei& Jiao, Weidong& Li, Kang& Irwin, George. 2013. A Novel Sparse Least Squares Support Vector Machines. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1010039
Modern Language Association (MLA)
Xia, Xiao-Lei…[et al.]. A Novel Sparse Least Squares Support Vector Machines. Mathematical Problems in Engineering No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-1010039
American Medical Association (AMA)
Xia, Xiao-Lei& Jiao, Weidong& Li, Kang& Irwin, George. A Novel Sparse Least Squares Support Vector Machines. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1010039
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1010039