title: Laplacian twin support vector machine for semi-supervised classification.
abstract: 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.
authors.affiliation: Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing, China. qizhiquan@gucas.ac.cn
authors.forename: Zhiquan
authors.initials: Z
authors.lastname: Qi
authors.forename: Yingjie
authors.initials: Y
authors.lastname: Tian
authors.forename: Yong
authors.initials: Y
authors.lastname: Shi
mesh_terms: D000465:Algorithms; D057225:Data Mining; D003661:Decision Support Techniques; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D060388:Support Vector Machine