Parameterized semi-supervised classification based on support vector for multi-relational data

Authors:
Ling Ping;Zhou Chun-Guang
Affiliations:
College of Computer Science, Jilin University, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education, Changchun, China;College of Computer Science, Jilin University, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education, Changchun, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

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Abstract

A Parameterized Semi-supervised Classification algorithm based on Support Vector (PSCSV) for multi-relational data is presented in this paper. PSCSV produces class contours with support vectors, and further extracts center information of classes. Data is labeled according to its affinity to class centers. A novel Kernel function encoded in PSCSV is defined for multi-relational version and parameterized by supervisory information. Another point is the self learning of penalty parameter and Kernel scale parameter in the support-vector-based procedures, which eliminates the need to search parameter spaces. Experiments on real datasets demonstrate performance and efficiency of PSCSV.