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Volumn , Issue , 2009, Pages

Reliable agnostic learning

Author keywords

[No Author keywords available]

Indexed keywords

AGNOSTIC LEARNING; DNF FORMULAS; FALSE NEGATIVES; FALSE POSITIVE; HALF SPACES; MEMBERSHIP QUERY; NATURAL DISTRIBUTION; SPAM DETECTION;

EID: 84898064098     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (11)

References (14)
  • 1
    • 27744586991 scopus 로고    scopus 로고
    • Learning with the neyman-pearson and min-max criteria
    • Los Alamos National Laboratory
    • A. Cannon, J. Howse, D. Hush, and C. Scovel. Learning with the neyman-pearson and min-max criteria. Technical Report LA-UR-02-2951, Los Alamos National Laboratory, 2002.
    • (2002) Technical Report LA-UR-02-2951
    • Cannon, A.1    Howse, J.2    Hush, D.3    Scovel, C.4
  • 6
    • 0002192516 scopus 로고
    • Decision-theoretic generalizations of the PAC model for neural networks and other applications
    • D. Haussler. Decision-theoretic generalizations of the PAC model for neural networks and other applications. Information and Computation, 100: 78-150, 1992.
    • (1992) Information and Computation , vol.100 , pp. 78-150
    • Haussler, D.1
  • 7
    • 0031339159 scopus 로고    scopus 로고
    • An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
    • J.C. Jackson. An efficient membership-query algorithm for learning DNF with respect to the uniform distribution. Journal of Computer and System Sciences, 55(3): 414-440, 1997.
    • (1997) Journal of Computer and System Sciences , vol.55 , Issue.3 , pp. 414-440
    • Jackson, J.C.1
  • 11
    • 34547481510 scopus 로고    scopus 로고
    • On the use of roc analysis for the optimization of abstaining classifiers
    • Tadeusz Pietraszek. On the use of roc analysis for the optimization of abstaining classifiers. Machine Learning, 68(2): 137-169, 2007.
    • (2007) Machine Learning , vol.68 , Issue.2 , pp. 137-169
    • Pietraszek, T.1
  • 12
    • 27744553952 scopus 로고    scopus 로고
    • A neyman-pearson approach to statistical learning
    • C. Scott and R. Nowak. A neyman-pearson approach to statistical learning. IEEE Transactions on Information Theory, 51(11): 3806-3819, 2005.
    • (2005) IEEE Transactions on Information Theory , vol.51 , Issue.11 , pp. 3806-3819
    • Scott, C.1    Nowak, R.2
  • 13
    • 0021518106 scopus 로고
    • A theory of the learnable
    • L.G. Valiant. A theory of the learnable. Communications of the ACM, 27(11): 1134-1142, 1984.
    • (1984) Communications of the ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant, L.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.