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Volumn 6, Issue , 2005, Pages

Tutorial on practical prediction theory for classification

Author keywords

Classification; Quantitative bounds; Sample complexity bounds

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; ONLINE SYSTEMS; PROBABILITY; SET THEORY;

EID: 21844462365     PISSN: 15337928     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review
Times cited : (276)

References (25)
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    • Chernoff, H.1
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    • The use of confidence intervals for fiducial limits illustrated in the case of the binomial
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    • Clopper, C.J.1    Pearson, E.S.2
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    • T. Joachims, program SVMlightz
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    • Additive versus exponentiated gradient updates for linear prediction
    • J. Kivinen and M. Warmuth. Additive versus exponentiated gradient updates for linear prediction. Information and Computation, 132(1): 1-64, 1997.
    • (1997) Information and Computation , vol.132 , Issue.1 , pp. 1-64
    • Kivinen, J.1    Warmuth, M.2
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    • J. Langford. Program bound
    • J. Langford. Program bound.
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    • Microchoice bounds and self bounding learning algorithms
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    • Langford, J.1    Blum, A.2
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    • Bounds for averaging classifiers
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    • J. Langford and M. Seeger. Bounds for averaging classifiers. Technical report, Carnegie Mellon, Department of Computer Science, 2001.
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    • Langford, J.1    Seeger, M.2
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    • PAC-Bayesian generalization error bounds for gaussian process classification
    • M. Seeger. PAC-Bayesian generalization error bounds for gaussian process classification. Journal of Machine Learning Research, 3:233-269, 2002.
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    • Seeger, M.1
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    • S. Seung. Unpublished notes
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    • On the uniform convergence of relative frequencies of events to their probabilities
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    • Vapnik, V.N.1    Chervonenkis, A.Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.