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Volumn 34, Issue 4, 2004, Pages 383-392

A fixed-point algorithm to minimax learning with neural networks

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; ESTIMATION; LEARNING ALGORITHMS; PATTERN RECOGNITION; PROBABILITY;

EID: 7744240194     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCC.2004.833284     Document Type: Article
Times cited : (7)

References (17)
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    • Addressing the curse of imbalanced training sets: One-sided selection
    • San Mateo, CA
    • M. Kubat and S. Matwin, "Addressing the curse of imbalanced training sets: one-sided selection," in [i Proc. 14th Int. Conf. Machine Learning i]. San Mateo, CA, 1997, pp. 179-186.
    • (1997) Proc. 14th Int. Conf. Machine Learning , pp. 179-186
    • Kubat, M.1    Matwin, S.2
  • 3
    • 0001924639 scopus 로고    scopus 로고
    • Neural network classification and unequal prior class probabilities
    • ser. Lecture Notes in Computer Science State-of-the-Art Surveys, G. Orr, K.-R. Müller, and R. Caruana, Eds. New York: Springer-Verlag
    • S. Lawrence, I. Burns, A. Back, A. Tsoi, and C. L. Giles, "Neural network classification and unequal prior class probabilities," in [i Tricks of the Trade i]. ser. Lecture Notes in Computer Science State-of-the-Art Surveys, G. Orr, K.-R. Müller, and R. Caruana, Eds. New York: Springer-Verlag, 1998, pp. 299-314.
    • (1998) Tricks of the Trade , pp. 299-314
    • Lawrence, S.1    Burns, I.2    Back, A.3    Tsoi, A.4    Giles, C.L.5
  • 5
    • 0036134369 scopus 로고    scopus 로고
    • Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure
    • Jan
    • M. Saerens, P. Latinne, and C. Decaestecker, "Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure," [i Neural Comput. i], vol. 14, no. 1, pp. 21-41, Jan. 2002.
    • (2002) Neural Comput. , vol.14 , Issue.1 , pp. 21-41
    • Saerens, M.1    Latinne, P.2    Decaestecker, C.3
  • 6
    • 0035283313 scopus 로고    scopus 로고
    • Robust classification systems for imprecise environments
    • Mar
    • F. J. Provost and T. Fawcett, "Robust classification systems for imprecise environments," [i Mach. Learn. i], vol. 42, no. 3, pp. 203-231, Mar. 2001.
    • (2001) Mach. Learn. , vol.42 , Issue.3 , pp. 203-231
    • Provost, F.J.1    Fawcett, T.2
  • 8
    • 0031998121 scopus 로고    scopus 로고
    • Machine learning for the detection of oil spills in satellite radar images
    • M. Kubat, R. Holte, and S. Matwin, "Machine learning for the detection of oil spills in satellite radar images," [i Mach. Learn. i], vol. 30, no. 2, 3, pp. 195-215, 1998.
    • (1998) Mach. Learn. , vol.30 , Issue.2-3 , pp. 195-215
    • Kubat, M.1    Holte, R.2    Matwin, S.3
  • 10
    • 0013316935 scopus 로고    scopus 로고
    • Adacost: Misclassification cost-sensitive boosting
    • C. Elkan, "Adacost: misclassification cost-sensitive boosting," in [i Proc. 16th Int. Conf. Mach. Learn. i], 1999, pp. 97-105.
    • (1999) Proc. 16th Int. Conf. Mach. Learn. , pp. 97-105
    • Elkan, C.1
  • 15
    • 0035330125 scopus 로고    scopus 로고
    • On the structure of strict sense Bayesian cost functions and its applications
    • May
    • J. Cid-Sueiro and A. Figueiras-Vidal, "On the structure of strict sense Bayesian cost functions and its applications," [i IEEE Trans. Neural Networks i], vol. 12, pp. 445-455, May 2001.
    • (2001) IEEE Trans. Neural Networks , vol.12 , pp. 445-455
    • Cid-Sueiro, J.1    Figueiras-Vidal, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.