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Volumn 20, Issue 11-13, 1999, Pages 1183-1190

On global, local, mixed and neighborhood kernels for support vector machines

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FEATURE EXTRACTION; IMAGE ANALYSIS;

EID: 0033220805     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(99)00086-0     Document Type: Article
Times cited : (47)

References (15)
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    • Barzilay, O.1    Brailovsky, V.L.2
  • 2
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    • Bottou, L., Vapnik, V., 1992. Local learning algorithm. Neural Computations 4 (6), 888-900.
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    • Bottou, L.1    Vapnik, V.2
  • 3
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    • Support-vector networks
    • Cortes, C., Vapnik, V., 1995. Support-vector networks. Machine Learning 20, 273-297.
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 5
    • 20444440880 scopus 로고
    • Random field models in image analysis
    • Dubes, R.C., Jain, A.K., 1989. Random field models in image analysis. J. Appl. Statist. 16 (2).
    • (1989) J. Appl. Statist. , vol.16 , Issue.2
    • Dubes, R.C.1    Jain, A.K.2
  • 8
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale SVM learning practical
    • Christopher, B.S., Burges, J.C., Smola, A.J. (Eds.), MIT Press, Cambridge, USA
    • Joachims, T., 1998. Making large-scale SVM learning practical. In: Christopher, B.S., Burges, J.C., Smola, A.J. (Eds.), Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, USA.
    • (1998) Advances in Kernel Methods - Support Vector Learning
    • Joachims, T.1
  • 9
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    • Improving the k-NCN classification rule through heuristic modifications
    • Sánchez, J.S., Pla, F., Ferri, F.J., 1998. Improving the k-NCN classification rule through heuristic modifications. Pattern Recognition Letters 19, 1165-1170.
    • (1998) Pattern Recognition Letters , vol.19 , pp. 1165-1170
    • Sánchez, J.S.1    Pla, F.2    Ferri, F.J.3
  • 10
    • 84902142380 scopus 로고    scopus 로고
    • Incorporating invariances in support vector learning machines
    • von der Malsburg, von Seelen, W., Vortbruggen, J.C., Sendhoff, B. (Eds.), Artificial Neural Networks - ICANN'96 Berlin
    • Scholkoph, B., Burges, C., Vapnik, V., 1996a. Incorporating invariances in support vector learning machines. In: von der Malsburg, von Seelen, W., Vortbruggen, J.C., Sendhoff, B. (Eds.), Artificial Neural Networks - ICANN'96. Springer Lecture Notes in Computer Science, Vol. 1112, Berlin, pp. 47-52.
    • (1996) Springer Lecture Notes in Computer Science , vol.1112 , pp. 47-52
    • Scholkoph, B.1    Burges, C.2    Vapnik, V.3
  • 11
    • 0003836788 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • Max-Plank-Institute for Biological Kybernetik, Technical Report No. 44
    • Scholkoph, B., Smola, A., Muller, K.-S., 1996b. Nonlinear component analysis as a kernel eigenvalue problem. Max-Plank-Institute for Biological Kybernetik, Technical Report No. 44 (see also Neural Computation 10, 1998 and http:// svm.first.gmd.de).
    • (1996) Neural Computation , vol.10
    • Scholkoph, B.1    Smola, A.2    Muller, K.-S.3
  • 13
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    • Prior knowledge in support vector kernels
    • Jordan, M., Kearns, M., Solla, S. (Eds.), MIT Press, Cambridge, MA
    • Scholkoph, B., Simard, P., Smola, A., Vapnik, V., 1998b. Prior knowledge in support vector kernels. In: Jordan, M., Kearns, M., Solla, S. (Eds.), Advances in Neural Information Processing Systems, Vol. 10. MIT Press, Cambridge, MA.
    • (1998) Advances in Neural Information Processing Systems , vol.10
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  • 15
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    • Support vector method for function approximation, regression estimation and signal processing
    • Vapnik, V., Golowich, S., Smola, A., 1996. Support vector method for function approximation, regression estimation and signal processing. Proc. NISP'96. See also http:// svm.first.gmd.de.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.