A Spectrum-Based Support Vector Algorithm for Relational Data Semi-supervised Classification

  • Ling Ping
  • Wang Zhe
  • Zhou Chunguang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


A Spectrum-based Support Vector Algorithm (SSVA) to resolve semi-supervised classification for relational data is presented in this paper. SSVA extracts data representatives and groups them with spectral analysis. Label assignment is done according to affinities between data and data representatives. The Kernel function encoded in SSVA is defined to rear to relational version and parameterized by supervisory information. Another point is the self-tuning of penalty coefficient and Kernel scale parameter to eliminate the need of searching parameter spaces. Experiments on real datasets demonstrate the performance and efficiency of SSVA.


Gaussian Kernel Spectral Cluster Neural Information Processing System Penalty Coefficient Main Table 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Ping
    • 1
    • 2
  • Wang Zhe
    • 1
  • Zhou Chunguang
    • 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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