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Parameterized Semi-supervised Classification Based on Support Vector for Multi-relational Data

  • Ling Ping
  • Zhou Chun-Guang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

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.

Keywords

Penalty Parameter Class Center Main Table Supervisory Information Elementary Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Ping
    • 1
    • 2
  • Zhou Chun-Guang
    • 1
  1. 1.College of Computer ScienceJilin University, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of EducationChangchunChina
  2. 2.School of Computer ScienceXuzhou Normal UniversityXuzhouChina

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