Applied Mathematics & Information Sciences
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
Recommended Citation
Zhang, Chun; Yang, Junan; Zhang, Jiyang; Li, Dongsheng; and Yong, Aixia
(2014)
"Semi-Supervised Learning by Local Behavioral Searching Strategy,"
Applied Mathematics & Information Sciences: Vol. 08:
Iss.
4, Article 35.
DOI: http://dx.doi.org/10.12785/amis/080435
Available at:
https://digitalcommons.aaru.edu.jo/amis/vol08/iss4/35