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Volumn 1, Issue , 2006, Pages 564-568

Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features

Author keywords

Meta learning; Parameter selection; Support vector machines

Indexed keywords

ALGORITHMS; PARAMETER ESTIMATION; REGRESSION ANALYSIS; STATISTICAL METHODS;

EID: 33751067274     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1141277.1141408     Document Type: Conference Paper
Times cited : (26)

References (15)
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  • 2
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  • 4
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    • Collobert, R.1    Bengio, S.2
  • 5
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    • Dynamically adapting kernels in support vector machines
    • M. Kearns, S. Solla, and D. Cohn, editors. MIT Press
    • N. Cristianini, J. Shawe-Taylor, and C. Campbell. Dynamically adapting kernels in support vector machines. In M. Kearns, S. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems, volume 11, pages 204-210. MIT Press, 1998.
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    • Cristianini, N.1    Shawe-Taylor, J.2    Campbell, C.3
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    • Using the fisher kernel method to detect remote protein homologies
    • T. Lengauer, R. Schneider, P. Bork, D. Brutlag, J. Glasgow, M. Mewes, and R. Zimmer, editors. AAAI Press
    • T. Jaakkola, M. Diekhans, and D. Haussler. Using the fisher kernel method to detect remote protein homologies. In T. Lengauer, R. Schneider, P. Bork, D. Brutlag, J. Glasgow, M. Mewes, and R. Zimmer, editors, Proc. of the Seventh Int. Conf. on Intelligent Systems for Molecular Biology, pages 149-158. AAAI Press, 1999.
    • (1999) Proc. of the Seventh Int. Conf. on Intelligent Systems for Molecular Biology , pp. 149-158
    • Jaakkola, T.1    Diekhans, M.2    Haussler, D.3
  • 15
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    • A meta-learning method to select the kernel width in support vector regression
    • C. Soares, P. Brazdil, and P. Kuba. A meta-learning method to select the kernel width in support vector regression. Machine Learning, 54:195-209, 2004.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.