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Volumn 3559 LNAI, Issue , 2005, Pages 127-142

Generalization error bounds using unlabeled data

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; CLASSIFIERS; DATA REDUCTION; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; PROBABILITY;

EID: 26944465352     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11503415_9     Document Type: Conference Paper
Times cited : (34)

References (19)
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    • Madani, O.1    Pennock, D.M.2    Flake, G.W.3
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    • Metric-based methods for adaptive model selection and regularization
    • Schuurmans, D., Southey, F.: Metric-based methods for adaptive model selection and regularization. Machine Learning 42 (2002) 51-84
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    • Schuurmans, D.1    Southey, F.2
  • 9
    • 0038453192 scopus 로고    scopus 로고
    • Rademacher and Gaussian complexities: Risk bounds and structural results
    • Bartlett, P.L., Mendelson, S.: Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research 3 (2002) 463-482
    • (2002) Journal of Machine Learning Research , vol.3 , pp. 463-482
    • Bartlett, P.L.1    Mendelson, S.2
  • 10
    • 26944441121 scopus 로고    scopus 로고
    • Relating the Rademacher and VC bounds
    • Department of Computer Science, Series of Publications C
    • Kääriäinen, M.: Relating the Rademacher and VC bounds. Technical Report Report C-2004-57, Department of Computer Science, Series of Publications C (2004)
    • (2004) Technical Report Report , vol.C-2004-57
    • Kääriäinen, M.1
  • 11
  • 12
    • 0037399538 scopus 로고    scopus 로고
    • PAC-Bayesian stochastic model selection
    • McAllester, D.A.: PAC-Bayesian stochastic model selection. Machine Learning 51 (2003) 5-21
    • (2003) Machine Learning , vol.51 , pp. 5-21
    • McAllester, D.A.1
  • 15
    • 0003336572 scopus 로고    scopus 로고
    • A probabilistic theory of pattern recognition
    • Springer, Berlin Heidelberg New York
    • Devroye, L., Györfi, L., Lugosi, G.: A Probabilistic Theory of Pattern Recognition. Volume 31 of Applications of Mathematics. Springer, Berlin Heidelberg New York (1996)
    • (1996) Applications of Mathematics , vol.31
    • Devroye, L.1    Györfi, L.2    Lugosi, G.3
  • 16
    • 84880904019 scopus 로고    scopus 로고
    • Practical prediction theory for classification
    • A tutorial presented
    • Langford, J.: Practical prediction theory for classification (2003) A tutorial presented at ICML 2003. Available at http://hunch.net/~jl/projects/ prediction_bounds/tutorial/tutorial.pdf.
    • (2003) ICML 2003
    • Langford, J.1
  • 17
    • 3142674150 scopus 로고    scopus 로고
    • Almost-everywhere algorithmic stability and generalization error
    • Kutin, S., Niyogi, P.: Almost-everywhere algorithmic stability and generalization error. In: Proceedings of Uncertainty in AI. (2002) 275-282
    • (2002) Proceedings of Uncertainty in AI , pp. 275-282
    • Kutin, S.1    Niyogi, P.2
  • 18
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale SVM learning practical
    • Schölkopf, B., Burges, C., Smola, A., eds.: MIT-Press
    • Joachims, T.: Making large-scale SVM learning practical. In Schölkopf, B., Burges, C., Smola, A., eds.: Advances in Kernel Methods - Support Vector Learning. MIT-Press (1999)
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    • Joachims, T.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.