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Semi-Supervised Learning by Local Behavioral Searching Strategy |
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PP: 1781-1787 |
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Author(s) |
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Chun Zhang,
Junan Yang,
Jiyang Zhang,
Dongsheng Li,
Aixia Yong,
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Abstract |
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| 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. |
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