Journal
NEURAL NETWORKS
Volume 35, Issue -, Pages 46-53Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2012.07.011
Keywords
Semi-supervised classification; Laplacian; Twin support vector machine; Multi-class classification
Funding
- National Natural Science Foundation of China [70921061, 10601064]
- CAS/SAFEA [71110107026]
- GUCAS
- National Technology Support Program [2009-BAH42B02]
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Semi-supervised learning has attracted a great deal of attention in machine learning and data mining. In this paper, we have proposed a novel Laplacian Twin Support Vector Machine (called Lap-TSVM) for the semi-supervised classification problem, which can exploit the geometry information of the marginal distribution embedded in unlabeled data to construct a more reasonable classifier and be a useful extension of TSVM. Furthermore, by choosing appropriate parameters, Lap-TSVM degenerates to either TSVM or TBSVM. All experiments on synthetic and real data sets show that the Lap-TSVM's classifier combined by two nonparallel hyperplanes is superior to Lap-SVM and TSVM in both classification accuracy and computation time. (C) 2012 Elsevier Ltd. All rights reserved.
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