Semi-supervised behavioral learning and its application

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• 作者：Chun Zhang^a ; ^b ; ^{zhangchuncwins@163.com" class="auth_mail" title="E-mail the corresponding author} ; Shafei Wang^c ; Dongsheng Li^a ; ^b ; Junan Yang^a ; ^b ; Jiyang Zhang^d
• 关键词：Semi-supervised learning ; Behavioral learning ; Regularization ; Manifold learning ; Support vector machines
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：127
• 期：1
• 页码：376-382
• 全文大小：902 K

文摘

Semi-supervised learning has attracted significant attention in pattern recognition and machine learning. Among these methods, a very popular type is semi-supervised support vector machines. However, parameter selection in heat kernel function during the learning process is troublesome and harms the performance improvement of the hypothesis. To solve this problem, a novel local behavioral searching strategy is proposed for semi-supervised learning in this paper. In detail, based on human behavioral learning theory, the support vector machine is regularized with the un-normalized graph Laplacian. After building local distribution of feature space, local behavioral paradigm considers the form of the underlying probability distribution in the neighborhood of a point. Validation of the proposed method is performed with toy and real-life data sets. Results demonstrate that compared with traditional method, our method can more effectively and stably enhance the learning performance.