A weighted L-q adaptive least squares support vector machine classifiers - Robust and sparse approximation
Liu, JL; Li, JP; Xu, WX; Shi, Y
2011
Source PublicationEXPERT SYSTEMS WITH APPLICATIONS
ISSN0957-4174
Volume38Issue:3Pages:7,2253-2259
AbstractThe standard Support Vector Machine (SVM) minimizes the c-insensitive loss function subject to L-2 penalty, which equals solving a quadratic programming. While the least squares support vector machine (LS-SVM) considers equality constraints instead of inequality constrains, which corresponds to solving a set of linear equations to reduce computational complexity, loses sparseness and robustness. These two learning methods are non-adaptive since their penalty functions are pre-defined in a top-down manner, which do not work well in all situations. In this paper, we try to solve these two drawbacks and propose a weighted L-q adaptive LS-SVM model (WLq-LS-SVM) classifiers that combines the prior knowledge and adaptive learning process, which adaptively chooses q according to the data set structure. An evolutionary strategy-based algorithm is suggested to solve the WLq-LS-SVM. Simulation and real data tests have shown the effectiveness of our method. (C) 2010 Elsevier Ltd. All rights reserved.
KeywordLeast Squares Support Vector Machine Weight Adaptive Penalty Classification Robust Sparse
Subject AreaComputer Science ; Artificial Intelligence ; Electrical & Electronic ; Engineering ; Operations Research & Management Science
Indexed BySCI
Language英语
WOS IDWOS:000284863200106
Citation statistics
Cited Times:29[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.casisd.cn/handle/190111/4331
Collection中国科学院科技政策与管理科学研究所(1985年6月-2015年12月)
Recommended Citation
GB/T 7714
Liu, JL,Li, JP,Xu, WX,et al. A weighted L-q adaptive least squares support vector machine classifiers - Robust and sparse approximation[J]. EXPERT SYSTEMS WITH APPLICATIONS,2011,38(3):7,2253-2259.
APA Liu, JL,Li, JP,Xu, WX,&Shi, Y.(2011).A weighted L-q adaptive least squares support vector machine classifiers - Robust and sparse approximation.EXPERT SYSTEMS WITH APPLICATIONS,38(3),7,2253-2259.
MLA Liu, JL,et al."A weighted L-q adaptive least squares support vector machine classifiers - Robust and sparse approximation".EXPERT SYSTEMS WITH APPLICATIONS 38.3(2011):7,2253-2259.
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