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Wireless Personal Communications
Published in: Wireless Personal Communications 1/2018

05-02-2018

Weighted Least Squares Support Vector Machine for Semi-supervised Classification

Authors: Zhanwei Liu, Houquan Liu, Zhikai Zhao

Published in: Wireless Personal Communications | Issue 1/2018

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Abstract

The recently proposed semi-supervised least squares support vector machine (SLS-SVM), extends support vector machine (SVM) to semi-supervised learning field. However, the support value in SLS-SVM is not zero and the solution is lack of sparseness. To overcome this drawback, a weighted semi-supervised SLS-SVM (WSLS-SVM) is proposed in this paper, where the impact of labeled and unlabeled samples can be controlled by weighting the corresponding error. It is basically a pruning method according to the sorted weight of estimation error. To solve the proposed classifier, an efficient progressive learning algorithm is presented to reduce the iteration. Experimental results on several benchmarks data sets confirm the sparseness and the effectiveness of the proposed method.
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Metadata
Title
Weighted Least Squares Support Vector Machine for Semi-supervised Classification
Authors
Zhanwei Liu
Houquan Liu
Zhikai Zhao
Publication date
05-02-2018
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 1/2018
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-018-5478-y

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