Semi-supervised learning has attracted a significant amount of 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 extensive experiments. Results demonstrate that compared with traditional method, our method can more effectively and stably enhance the learning performance.
Digital Object Identifier (DOI)
Zhang, Chun; Yang, Junan; Zhang, Jiyang; Li, Dongsheng; and Yong, Aixia
"Semi-Supervised Learning by Local Behavioral Searching Strategy,"
Applied Mathematics & Information Sciences: Vol. 08
, Article 35.
Available at: https://dc.naturalspublishing.com/amis/vol08/iss4/35