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Volumn 3, Issue , 2005, Pages 1443-1448

Fβ support vector machines

Author keywords

[No Author keywords available]

Indexed keywords

GRADIENT METHODS; INFORMATION RETRIEVAL; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION;

EID: 33750107487     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2005.1556087     Document Type: Conference Paper
Times cited : (6)

References (21)
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  • 5
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    • Radius margin bounds for support vector machines with the rbf kernel
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    • Chung, K.-M.1    Kao, W.-C.2    Sun, C.-L.3    Wang, L.-L.4    Lin, C.-J.5
  • 6
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    • Support-vector networks
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    • Cortes, C.1    Vapnik, V.2
  • 7
    • 0037382208 scopus 로고    scopus 로고
    • Evaluation of simple performance measures for tuning svm hyperparameters
    • Kaibo Duan, S. Sathiya Keerthi, and Aun Neow Poo. Evaluation of simple performance measures for tuning svm hyperparameters. Neurocomputing, 51:41-59, 2003.
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    • Duan, K.1    Keerthi, S.S.2    Aun Neow Poo3
  • 8
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    • Fletcher, R.1    Powell, M.J.D.2
  • 9
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale support vector machine learning practical
    • A. Smola B. Scholkopf, C. Burges, editor. MIT Press, Cambridge, MA
    • T. Joachims. Making large-scale support vector machine learning practical. In A. Smola B. Scholkopf, C. Burges, editor, Advances in Kernel Methods: Support Vector Machines. MIT Press, Cambridge, MA, 1998.
    • (1998) Advances in Kernel Methods: Support Vector Machines
    • Joachims, T.1
  • 10
    • 0003307180 scopus 로고    scopus 로고
    • Estimating the generalization performance of a SVM efficiently
    • Pat Langley, editor, Stanford, US. Morgan Kaufmann Publishers, San Francisco, US
    • Thorsten Joachims. Estimating the generalization performance of a SVM efficiently. In Pat Langley, editor, Proceedings of ICML-00, 17th International Conference on Machine Learning, pages 431-438, Stanford, US, 2000. Morgan Kaufmann Publishers, San Francisco, US.
    • (2000) Proceedings of ICML-00, 17th International Conference on Machine Learning , pp. 431-438
    • Joachims, T.1
  • 11
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    • A fast iterative nearest point algorithm for support vector machine classifier design
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    • S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy. A fast iterative nearest point algorithm for support vector machine classifier design. IEEE-NN, 11(1):124, January 2000.
    • (2000) IEEE-NN , vol.11 , Issue.1 , pp. 124
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    • Generalization bounds via eigenvalues of the Gram matrix
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.