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Volumn , Issue , 2008, Pages 479-490

On the margin explanation of boosting algorithms

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; BOOSTING ALGORITHM; GENERALIZATION ERROR; MARGIN BOUNDS; MARGIN DISTRIBUTIONS; MARGIN THEORY; TRAINING DATA; VOTING CLASSIFIERS;

EID: 80053436012     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (51)

References (24)
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    • Bounds for the uniform deviation of empirical measures
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    • Devroye, L.1
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    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization
    • T. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Machine Learning, 40: 139-157, 2000.
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    • Dietterich, T.1
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    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55: 119-139, 1997.
    • (1997) Journal of Computer and System Sciences , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 12
    • 84947403595 scopus 로고
    • Probability inequalities for sum of bounded random variables
    • W. Hoeffding. Probability inequalities for sum of bounded random variables. Journal of American Statistical Society, 58: 13-30, 1963.
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    • Hoeffding, W.1
  • 13
    • 0036104545 scopus 로고    scopus 로고
    • Empirical margin distributions and bounding the generalization error of combined classifiers
    • V. Koltchinskii and D. Panchanko. Empirical margin distributions and bounding the generalization error of combined classifiers. Annals of Statistics, 30: 1-50, 2002.
    • (2002) Annals of Statistics , vol.30 , pp. 1-50
    • Koltchinskii, V.1    Panchanko, D.2
  • 14
    • 26444607491 scopus 로고    scopus 로고
    • Complexities of convex combinations and bounding the generalization error in classification
    • V. Koltchinskii and D. Panchanko. Complexities of convex combinations and bounding the generalization error in classification. Annals of Statistics, 33: 1455-1496, 2005.
    • (2005) Annals of Statistics , vol.33 , pp. 1455-1496
    • Koltchinskii, V.1    Panchanko, D.2
  • 15
    • 21844462365 scopus 로고    scopus 로고
    • Tutorial on practical prediction theory for classification
    • J. Langford. Tutorial on practical prediction theory for classification. Journal of Machine Learning Research, 6: 273-306, 2005.
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    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • R. Schapire, Y. Freund, P. Bartlett, and W. Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, 26: 1651-1686, 1998.
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    • Schapire, R.1    Freund, Y.2    Bartlett, P.3    Lee, W.4
  • 23
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    • Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities
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    • Vapnik, V.N.1    Ya, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.