NDLI: Hybrid robust LS-SVMR with outliers for MIMO system

Content Provider	IEEE Xplore Digital Library
Author	Tao, C.W. Chen-Chia Chuang Meng-Hua Lai Song-Shyong Chen Jin-Tsong Jeng
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering, National Ilan University, 1, Sec. 1, Shen-Lung Road, TAIWAN 260 (Tao, C.W.; Chen-Chia Chuang; Meng-Hua Lai) \|\| Department of Information Networking technology, Hsiuping Institute of Technology, Taiwan (Song-Shyong Chen) \|\| Department of Computer Science and Information Engineering, National Formosa University, China (Jin-Tsong Jeng)
Abstract	In this study, a hybrid robust LS-SVMR approach is proposed to deal with training data sets with outliers dor MIMO system. The proposed approach consists of two stages of strategies. The first stage is for data preprocessing and a support vector regression is used to filter out outliers. Then, the training data set except for outliers, called as the reduced training data set, is directly used in training the non-robust least squares support vector machines for regression (LS-SVMR) for MIMO system in the second stage. 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 with non-robust LS-SVMR is superior to the weighted LS-SVMR approach for MIMO system when the outliers exist.
Starting Page	3839
Ending Page	3844
File Size	944515
Page Count	6
File Format	PDF
ISBN	9781424465866
ISSN	1062922X
e-ISBN	9781424465880
DOI	10.1109/ICSMC.2010.5641970
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-10
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Indexes Support vector machines Weight measurement Weighted LS-SVMR LS-SVMR Outliers Suport vector regression
Content Type	Text
Resource Type	Article

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