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Volumn , Issue , 2007, Pages 1125-1130

A subspace kernel for nonlinear feature extraction

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING TASKS; DIMENSIONALITY REDUCTION; HIGH DIMENSIONALITY; KERNEL PARAMETER; LOW-DIMENSIONAL SUBSPACE; NONLINEAR FEATURE EXTRACTION; POSITIVE DEFINITE KERNELS; PRE-PROCESSING STEP;

EID: 48149102643     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (7)

References (12)
  • 1
    • 0036161011 scopus 로고    scopus 로고
    • Choosing multiple parameters for support vector machines
    • DOI 10.1023/A:1012450327387
    • O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46(1-3):131-159, 2002. (Pubitemid 34129966)
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 131-159
    • Chapelle, O.1    Vapnik, V.2    Bousquet, O.3    Mukherjee, S.4
  • 3
    • 21844475661 scopus 로고    scopus 로고
    • Dimension reduction in text classification with support vector machines
    • H. Kim, P. Howland, and H. Park. Dimension reduction in text classification with support vector machines. Journal of Machine Learning Research, 6:37-53, 2005.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 37-53
    • Kim, H.1    Howland, P.2    Park, H.3
  • 4
    • 84898965347 scopus 로고    scopus 로고
    • A mathematical programming approach to the kernel fisher algorithm
    • T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Cambridge, MA, The MIT Press
    • S. Mika, G. Rätsch, and K. R. Müller. A mathematical programming approach to the kernel fisher algorithm. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, Cambridge, MA, 2001. The MIT Press.
    • (2001) Advances in Neural Information Processing Systems 13
    • Mika, S.1    Rätsch, G.2    Müller, K.R.3
  • 5
    • 1542316654 scopus 로고    scopus 로고
    • Nonlinear feature extraction based on centroids and kernel functions
    • DOI 10.1016/j.patcog.2003.07.011
    • C. H. Park and H. Park. Nonlinear feature extraction based on centroids and kernel functions. Pattern Recognition, 37:801-810, 2004. (Pubitemid 38301057)
    • (2004) Pattern Recognition , vol.37 , Issue.4 , pp. 801-810
    • Park, C.H.1    Park, H.2
  • 6
    • 0038259120 scopus 로고    scopus 로고
    • Kernel partial least squares regression in reproducing kernel hilbert space
    • R. Rosipal and L. J. Trejo. Kernel partial least squares regression in reproducing kernel hilbert space. Journal of Machine Learning Research, 2:97-123, 2001.
    • (2001) Journal of Machine Learning Research , vol.2 , pp. 97-123
    • Rosipal, R.1    Trejo, L.J.2
  • 10
    • 0002692783 scopus 로고
    • Soft modeling by latent variables; the nonlinear iterative partial least squares approach
    • J. Gani, editor, London, Academic Press
    • H. Wold. Soft modeling by latent variables; the nonlinear iterative partial least squares approach. In J. Gani, editor, Perspectives in Probability and Statistics, pages 520-540, London, 1975. Academic Press.
    • (1975) Perspectives in Probability and Statistics , pp. 520-540
    • Wold, H.1
  • 12
    • 84899008974 scopus 로고    scopus 로고
    • Efficient kernel discriminant analysis via QR decomposition
    • L. K. Saul, Y.Weiss, and L. Bottou, editors, MIT Press, Cambridge, MA
    • T. Xiong, J. Ye, Q. Li, R. Janardan, and V. Cherkassky. Efficient kernel discriminant analysis via QR decomposition. In L. K. Saul, Y.Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 1529-1536. MIT Press, Cambridge, MA, 2005.
    • (2005) Advances in Neural Information Processing Systems 17 , pp. 1529-1536
    • Xiong, T.1    Ye, J.2    Li, Q.3    Janardan, R.4    Cherkassky, V.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.