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Volumn 38, Issue 1, 2005, Pages 29-39

Minimax classifiers based on neural networks

Author keywords

Minimax decision rules; Neural networks; Pattern classification; Uncertainty in priors

Indexed keywords

ALGORITHMS; DECISION THEORY; ERROR ANALYSIS; NEURAL NETWORKS; PROBABILITY;

EID: 4644273126     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2004.05.007     Document Type: Article
Times cited : (10)

References (24)
  • 2
  • 3
    • 0001924639 scopus 로고    scopus 로고
    • Neural network classification and unequal prior class probabilities
    • G. Orr, K.-R. Müller, R. Caruana (Eds.), Tricks of the Trade, Springer, Berlin
    • S. Lawrence, I. Burns, A. Back, A. Tsoi, C.L. Giles, Neural network classification and unequal prior class probabilities, in: G. Orr, K.-R. Müller, R. Caruana (Eds.), Tricks of the Trade, Lecture Notes in Computer Science State-of-the-Art Surveys, Springer, Berlin, 1998, pp. 299-314.
    • (1998) Lecture Notes in Computer Science State-of-the-art Surveys , pp. 299-314
    • Lawrence, S.1    Burns, I.2    Back, A.3    Tsoi, A.4    Giles, C.L.5
  • 6
    • 1942482069 scopus 로고    scopus 로고
    • Learning with non-uniform class and cost distributions: Effects and a distributed multi-classifier approach
    • P. Chan, S. Stolfo, Learning with non-uniform class and cost distributions: effects and a distributed multi-classifier approach, in: Workshop Notes KDD-98 Workshop on Distributed Data Mining, 1998, pp. 1-9.
    • (1998) Workshop Notes KDD-98 Workshop on Distributed Data Mining , pp. 1-9
    • Chan, P.1    Stolfo, S.2
  • 7
    • 0036134369 scopus 로고    scopus 로고
    • Adjusting a classifier for new a priori probabilities: A simple procedure
    • M. Saerens, P. Latinne, C. Decaestecker, Adjusting a classifier for new a priori probabilities: a simple procedure, Neural Comput. 14 (2002) 21-41.
    • (2002) Neural Comput. , vol.14 , pp. 21-41
    • Saerens, M.1    Latinne, P.2    Decaestecker, C.3
  • 9
    • 0031998121 scopus 로고    scopus 로고
    • Machine learning for the detection of oil spills in satellite radar images
    • M. Kubat, R. Holte, S. Matwin, Machine learning for the detection of oil spills in satellite radar images, Mach. Learning 30 (2/3) (1998) 195-215.
    • (1998) Mach. Learning , vol.30 , Issue.2-3 , pp. 195-215
    • Kubat, M.1    Holte, R.2    Matwin, S.3
  • 10
    • 0035283313 scopus 로고    scopus 로고
    • Robust classification systems for imprecise environments
    • F.J. Provost, T. Fawcett, Robust classification systems for imprecise environments, Mach. Learning 42 (3) (2001) 203-231.
    • (2001) Mach. Learning , vol.42 , Issue.3 , pp. 203-231
    • Provost, F.J.1    Fawcett, T.2
  • 11
    • 0033164667 scopus 로고    scopus 로고
    • Comparing classifiers when the misallocation costs are uncertain
    • N.M. Adams, D.J. Hand, Comparing classifiers when the misallocation costs are uncertain, Pattern Recognition 32 (7) (1998) 1139-1147.
    • (1998) Pattern Recognition , vol.32 , Issue.7 , pp. 1139-1147
    • Adams, N.M.1    Hand, D.J.2
  • 18
    • 0030295792 scopus 로고    scopus 로고
    • On a constrained optimal rule for classification with unknown prior individual group membership
    • H.-J. Kim, On a constrained optimal rule for classification with unknown prior individual group membership, J. Multivariate Anal. 59 (2) (1996) 166-186.
    • (1996) J. Multivariate Anal. , vol.59 , Issue.2 , pp. 166-186
    • Kim, H.-J.1
  • 20
    • 0003790115 scopus 로고    scopus 로고
    • The effect of class distribution on classifier learning
    • Department of Computer Science, Rutgers University
    • G. Weiss, F. Provost, The effect of class distribution on classifier learning, in: Technical Report ML-TR 43, Department of Computer Science, Rutgers University, 2001.
    • (2001) Technical Report , vol.ML-TR 43
    • Weiss, G.1    Provost, F.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.