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Volumn 348, Issue 1, 2005, Pages 70-83

Learning from positive and unlabeled examples

Author keywords

Data mining; PAC learning; Semi supervised learning; Statistical query model

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; LEARNING ALGORITHMS; MATHEMATICAL MODELS; PROBLEM SOLVING; QUERY LANGUAGES; STATISTICAL METHODS;

EID: 27744465529     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tcs.2005.09.007     Document Type: Conference Paper
Times cited : (134)

References (18)
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    • Learning from noisy examples
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    • Angluin, D.1    Laird, P.2
  • 5
    • 0347369199 scopus 로고    scopus 로고
    • Pac learning with constant-partition classification noise and applications to decision tree induction
    • S.E. Decatur, Pac learning with constant-partition classification noise and applications to decision tree induction, in: Proc. 14th Internat. Conf. on Machine Learning, 1997, pp. 83-91.
    • (1997) Proc. 14th Internat. Conf. on Machine Learning , pp. 83-91
    • Decatur, S.E.1
  • 10
    • 17444367511 scopus 로고    scopus 로고
    • On the efficiency of noise-tolerant PAC algorithms derived from statistical queries
    • J. Jackson, On the efficiency of noise-tolerant PAC algorithms derived from statistical queries, in: Proc. 13th Annu. Conf. on Computational Learning Theory, 2000, pp. 7-15.
    • (2000) Proc. 13th Annu. Conf. on Computational Learning Theory , pp. 7-15
    • Jackson, J.1
  • 11
    • 0001938951 scopus 로고    scopus 로고
    • Transductive inference for text classification using support vector machines
    • T. Joachims, Transductive inference for text classification using support vector machines, in: Proc. 16th Internat. Conf. on Machine Learning, 1999, pp. 200-209.
    • (1999) Proc. 16th Internat. Conf. on Machine Learning , pp. 200-209
    • Joachims, T.1
  • 12
    • 0027188175 scopus 로고
    • Efficient noise-tolerant learning from statistical queries
    • M. Kearns, Efficient noise-tolerant learning from statistical queries, in: Proc. 25th ACM Symp. on the Theory of Computing, 1993, pp. 392-401.
    • (1993) Proc. 25th ACM Symp. on the Theory of Computing , pp. 392-401
    • Kearns, M.1
  • 14
    • 0002337827 scopus 로고    scopus 로고
    • Machine learning and data mining
    • T. Mitchell Machine learning and data mining Commun. ACM 42 11 1999 30 36
    • (1999) Commun. ACM , vol.42 , Issue.11 , pp. 30-36
    • Mitchell, T.1
  • 16
    • 0033886806 scopus 로고    scopus 로고
    • Text classification from labeled and unlabeled documents using em
    • K. Nigam, A.K. McCallum, S. Thrun, and T.M. Mitchell Text classification from labeled and unlabeled documents using EM Machine Learning 39 2/3 2000 103 134
    • (2000) Machine Learning , vol.39 , Issue.23 , pp. 103-134
    • Nigam, K.1    McCallum, A.K.2    Thrun, S.3    Mitchell, T.M.4
  • 18
    • 0021518106 scopus 로고
    • A theory of the learnable
    • L.G. Valiant A theory of the learnable Commun. ACM 27 11 1984 1134 1142
    • (1984) Commun. ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant, L.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.