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Volumn 36, Issue 3 PART 1, 2009, Pages 5745-5749

A geometric method for model selection in support vector machine

Author keywords

Geometric algorithm; Model selection; Optimize; Parameter; Support vector machine

Indexed keywords

APPROXIMATION ALGORITHMS; ERRORS; GEOMETRY; NUMERICAL METHODS; PARAMETER ESTIMATION; QUADRATIC PROGRAMMING;

EID: 58349089675     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.06.096     Document Type: Article
Times cited : (7)

References (12)
  • 1
    • 33751033588 scopus 로고    scopus 로고
    • Optimizing resources in model selection for support vector machine
    • Adankon M.M., and Cheriet M. Optimizing resources in model selection for support vector machine. Pattern Recognition 40 (2007) 953-963
    • (2007) Pattern Recognition , vol.40 , pp. 953-963
    • Adankon, M.M.1    Cheriet, M.2
  • 2
    • 22844442782 scopus 로고    scopus 로고
    • Automatic model selection for the optimization of the SVM kernels
    • Ayat N.E., Cheriet M., and Suen C.Y. Automatic model selection for the optimization of the SVM kernels. Pattern Recognition Computer Science 38 10 (2005) 1733-1745
    • (2005) Pattern Recognition Computer Science , vol.38 , Issue.10 , pp. 1733-1745
    • Ayat, N.E.1    Cheriet, M.2    Suen, C.Y.3
  • 3
    • 0036161011 scopus 로고    scopus 로고
    • Choosing multiple parameters for support vector machines
    • Chapelle O., Vapnik V., Bousquet O., et al. Choosing multiple parameters for support vector machines. Machine Learning 46 (2002) 131-159
    • (2002) Machine Learning , vol.46 , pp. 131-159
    • Chapelle, O.1    Vapnik, V.2    Bousquet, O.3
  • 5
    • 0141430928 scopus 로고    scopus 로고
    • Radius margin bounds for support vector machines with the RBF kernel
    • Chung K.M., Kao W.-C., Wang L.-L., et al. Radius margin bounds for support vector machines with the RBF kernel. Neural Computation 38 10 (2003) 2643-2681
    • (2003) Neural Computation , vol.38 , Issue.10 , pp. 2643-2681
    • Chung, K.M.1    Kao, W.-C.2    Wang, L.-L.3
  • 6
    • 13244270060 scopus 로고    scopus 로고
    • Applying support vector machines to predict building energy consumption in tropical region
    • Dong B., Cao C., and Lee S.E. Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings 37 (2005) 545-553
    • (2005) Energy and Buildings , vol.37 , pp. 545-553
    • Dong, B.1    Cao, C.2    Lee, S.E.3
  • 7
    • 33646516358 scopus 로고    scopus 로고
    • A geometric approach to support vector machine (SVM) classification
    • Mavroforakis M.E., and Theodoridis S. A geometric approach to support vector machine (SVM) classification. IEEE Transactions on Neural Networks 17 3 (2007) 671-682
    • (2007) IEEE Transactions on Neural Networks , vol.17 , Issue.3 , pp. 671-682
    • Mavroforakis, M.E.1    Theodoridis, S.2
  • 8
    • 0030673582 scopus 로고    scopus 로고
    • Osuna, E., Freund, R., & Girosi, F. (1997). Training support vector machines: An application to face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 130-136).
    • Osuna, E., Freund, R., & Girosi, F. (1997). Training support vector machines: An application to face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 130-136).
  • 9
    • 58349095091 scopus 로고    scopus 로고
    • Platt, J. (2000). Probabilistic outputs for support vector machine and comparison to regularized likelihood methods. In A.J. Smola, P. Barlett, B. Schöelkopf, D. Schuurmans (Eds), Advances in large margin classifiers (pp. 61-74).
    • Platt, J. (2000). Probabilistic outputs for support vector machine and comparison to regularized likelihood methods. In A.J. Smola, P. Barlett, B. Schöelkopf, D. Schuurmans (Eds), Advances in large margin classifiers (pp. 61-74).


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.