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Volumn 2, Issue , 2005, Pages 871-876

Yet faster method to optimize SVR hyperparameters based on minimizing cross-validation error

Author keywords

[No Author keywords available]

Indexed keywords

ACCELERATION; CONVERGENCE OF NUMERICAL METHODS; DATA HANDLING; ITERATIVE METHODS; NEURAL NETWORKS; VECTORS;

EID: 33745933843     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2005.1555967     Document Type: Conference Paper
Times cited : (8)

References (11)
  • 1
    • 0003710380 scopus 로고    scopus 로고
    • Libsvm - A library for support vector machines
    • National Taiwan Univ.
    • C.-C. Chang and C.-J. Lin. Libsvm - a library for support vector machines (http://www/csie.ntu.edu.tm/~cjIin/libsvm), Technical report, National Taiwan Univ. 2004.
    • (2004) Technical Report
    • Chang, C.-C.1    Lin, C.-J.2
  • 2
    • 0000913324 scopus 로고    scopus 로고
    • Svmtorch: Support vector machines for large-scale regression problems
    • R. Collobert and S. Bengio. Svmtorch: support vector machines for large-scale regression problems, J. of Machine Learning Research, Vol.1, No.2, pp.143-160, 2001.
    • (2001) J. of Machine Learning Research , vol.1 , Issue.2 , pp. 143-160
    • Collobert, R.1    Bengio, S.2
  • 3
    • 0141794580 scopus 로고    scopus 로고
    • Optimizing support vector regression hyperparameters based on cross-validation
    • K. Ito and R. Nakano. Optimizing support vector regression hyperparameters based on cross-validation, Proc. Int. Joint Conf. on Neural Networks (UCNN 2003), pp.2077-2082, 2003.
    • (2003) Proc. Int. Joint Conf. on Neural Networks (UCNN 2003) , pp. 2077-2082
    • Ito, K.1    Nakano, R.2
  • 4
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale support vector machine learning practical
    • MIT Press
    • T. Joachims. Making large-scale support vector machine learning practical, Advances in Kernel Methods - Support Vector Learning, pp. 169-184, MIT Press, 1999.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 169-184
    • Joachims, T.1
  • 7
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • MIT Press
    • J. Platt. Fast training of support vector machines using sequential minimal optimization, Advances in Kernel Methods - Support Vector Learning, pp.185-208, MIT Press, 1999.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 185-208
    • Platt, J.1
  • 8
    • 84942811623 scopus 로고    scopus 로고
    • Discovery of relevant weights by minimizing cross-validation error
    • Proc. PAKDD 2000
    • K. Saito and R. Nakano. Discovery of relevant weights by minimizing cross-validation error, Proc. PAKDD 2000, LNAI 1805, pp.372-375, 2000.
    • (2000) LNAI , vol.1805 , pp. 372-375
    • Saito, K.1    Nakano, R.2
  • 10
    • 0003401675 scopus 로고    scopus 로고
    • A tutorial on suppourt vector regression
    • NeuroCOLT2
    • A.J. Smola and B. Scholkopf. A tutorial on suppourt vector regression. Technical Report NC2-TR-1998-030, NeuroCOLT2, 1998.
    • (1998) Technical Report , vol.NC2-TR-1998-030
    • Smola, A.J.1    Scholkopf, B.2
  • 11
    • 0000629975 scopus 로고
    • Cross-validatory choice and assessment of statistical predictions
    • M. Stone. Cross-validatory choice and assessment of statistical predictions, J. of the Royal Statistical Society B, Vol.64, pp. 111-147, 1974.
    • (1974) J. of the Royal Statistical Society B , vol.64 , pp. 111-147
    • Stone, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.