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Volumn 39, Issue 1, 2005, Pages 1-21

A review of kernel Fisher discriminant analysis for statistical classification

Author keywords

Feature space; Generalization error; Input space; Kernel methods; Linear discriminant analysis

Indexed keywords


EID: 20444485094     PISSN: 0038271X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (2)

References (20)
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    • Getting to grips with support vector machines: Theory
    • Accepted for publication in the
    • KROON, S. and OMLIN, C.W. (2004a). Getting to grips with support vector machines: theory. Accepted for publication in the South African Statistical Journal.
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    • Kroon, S.1    Omlin, C.W.2
  • 8
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    • Getting to grips with support vector machines: Application
    • Accepted for publication in the
    • KROON, S. and OMLIN, C.W. (2004b). Getting to grips with support vector machines: application. Accepted for publication in the South African Statistical Journal.
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    • Kroon, S.1    Omlin, C.W.2
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    • Mika, S.1    Rätsch, G.2    Weston, J.3    Schölkopf, B.4    Müller, K.-R.5
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    • A mathematical programming approach to the kernel Fisher algorithm
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    • MIKA, S., RÄTSCH, G. and MÜLLER, K.-R. (2001). A mathematical programming approach to the kernel Fisher algorithm. In T.K. Leen, T.G. Dietterich and V. Tresp (Eds.), Advances in Neural Information Processing Systems, 13, 591-597, MIT Press, London.
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    • Bayesian framework for least squares support vector machine classifiers, Gaussian processes and kernel Fisher discriminant analysis
    • VAN GESTEL, T., SUYKENS, J.A.K., LANCKRIET, G., LAMBRECHTS, A., DE MOOR, B. and VANDEWALLE, J. (2002). Bayesian framework for least squares support vector machine classifiers, Gaussian processes and kernel Fisher discriminant analysis. Neural Computation, 14, 1115-1147.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.