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Author Country (or Countries)

China

Abstract

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.

Suggested Reviewers

N/A

Digital Object Identifier (DOI)

http://dx.doi.org/10.12785/amis/080435

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