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Volumn 3, Issue , 2005, Pages 1431-1436

Efficient parameter selection for support vector machines in classification and regression via model-based global optimization

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; GLOBAL OPTIMIZATION; LEARNING SYSTEMS; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

EID: 33750124478     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2005.1556085     Document Type: Conference Paper
Times cited : (97)

References (13)
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    • O. Chapelle and V. Vapnik. Model selection for Support Vector Machines. In S. Solla, T. Leen, and K.-R. Müller, editors, Adv. Neural Inf. Proc. Syst. 12, Cambridge, MA, 2000. MIT Press.
    • (2000) Adv. Neural Inf. Proc. Syst. , vol.12
    • Chapelle, O.1    Vapnik, V.2
  • 2
    • 0036161011 scopus 로고    scopus 로고
    • Choosing multiple parameters for support vector machines
    • O. Chapelle, V. Vapnik, O. Bousqet, and S. Mukherjee. Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 46(1):131 - 159, 2002.
    • (2002) Machine Learning , vol.46 , Issue.1 , pp. 131-159
    • Chapelle, O.1    Vapnik, V.2    Bousqet, O.3    Mukherjee, S.4
  • 3
    • 0346250790 scopus 로고    scopus 로고
    • Practical selection of svm parameters and noise estimation for svm regression
    • V. Cherkassky and Y. Ma. Practical selection of svm parameters and noise estimation for svm regression. Neural Networks, 17(1):113 - 126, 2004.
    • (2004) Neural Networks , vol.17 , Issue.1 , pp. 113-126
    • Cherkassky, V.1    Ma, Y.2
  • 4
    • 0038891993 scopus 로고    scopus 로고
    • Sparse online gaussian processes
    • L. Csato and M. Opper. Sparse online gaussian processes. Neural Computation, 14(3):641 - 669, 2002.
    • (2002) Neural Computation , vol.14 , Issue.3 , pp. 641-669
    • Csato, L.1    Opper, M.2
  • 7
    • 0000561424 scopus 로고    scopus 로고
    • Efficient global optimization of expensive black-box functions
    • D. Jones, M. Schonlau, and W. Welch. Efficient global optimization of expensive black-box functions. J. Global Optimization, 13:455 - 492, 1998.
    • (1998) J. Global Optimization , vol.13 , pp. 455-492
    • Jones, D.1    Schonlau, M.2    Welch, W.3
  • 8
    • 0034271876 scopus 로고    scopus 로고
    • The evidence framework applied to support vector machines
    • J. Kwok. The evidence framework applied to support vector machines. IEEE Transactions on Neural Networks, 11(5):1162 - 1173, 2000.
    • (2000) IEEE Transactions on Neural Networks , vol.11 , Issue.5 , pp. 1162-1173
    • Kwok, J.1
  • 10
    • 0037905968 scopus 로고    scopus 로고
    • Gaussian processes - A replacement for supervised neural networks?
    • Lecture note
    • D. MacKay. Gaussian Processes - A Replacement for Supervised Neural Networks? In Proc. Neural Inf. Proc. Syst., 1997. Lecture note.
    • (1997) Proc. Neural Inf. Proc. Syst.
    • MacKay, D.1
  • 11
    • 0141869869 scopus 로고    scopus 로고
    • A pattern search method for model selection of support vector regression
    • M. Momma and K. Bennett. A pattern search method for model selection of support vector regression. In SIAM Conf. on Data Mining, 2002.
    • (2002) SIAM Conf. on Data Mining
    • Momma, M.1    Bennett, K.2
  • 13
    • 0038582147 scopus 로고    scopus 로고
    • Prediction with gaussian processes: From linear regression to linear prediction and beyond
    • Aston University, UK
    • C. Willams. Prediction with gaussian processes: From linear regression to linear prediction and beyond. Technical Report NRG/97/012, Aston University, UK, 1997.
    • (1997) Technical Report , vol.NRG-97-012
    • Willams, C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.