Abstract
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.
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Ping, L., Zhe, W., Chunguang, Z. (2006). A Spectrum-Based Support Vector Algorithm for Relational Data Semi-supervised Classification. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_89
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DOI: https://doi.org/10.1007/11893028_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
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