NDLI: Robust least squares-support vector machines for regression with outliers

Content Provider	IEEE Xplore Digital Library
Author	Chen-Chia Chuang Jin-Tsong Jeng Mei-Lang Chan
Copyright Year	2008
Description	Author affiliation: Electr. Eng. Dept., Nat. Ilan Univ., Ilan (Chen-Chia Chuang)
Abstract	In this study, the robust least square support vector machines for regression (RLS-SVMR) is proposed to deal with training data set with outliers. There are two-stage strategies in the proposed approach. In the stage I, called as data preprocessing, the support vector regression (SVR) approach is used to filter out the outliers in the training data set. Due to the outliers in the training data set are removed, the concepts of robust statistic theory have no need to reduce the outlierpsilas effect. Then, the training data set except for outliers, called as the reduced training data set, is directly used to training the non-robust least squares support vector machines for regression (LS-SVMR) in the stage II. Consequently, the learning mechanism of the proposed approach is much easier than the weighted LS-SVMR approach. Based on the simulation results, the performance of the proposed approach is superior to the weighted LS-SVMR approach when the outliers are existed.
Starting Page	312
Ending Page	317
File Size	260594
Page Count	6
File Format	PDF
ISBN	9781424418183
ISSN	10987584
DOI	10.1109/FUZZY.2008.4630383
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy systems Conferences
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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