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Volumn , Issue , 2008, Pages 533-539

Boosting the minimum margin: LP Boost vs. Ada Boost

Author keywords

[No Author keywords available]

Indexed keywords

ADABOOST; CLASSIFICATION PERFORMANCE; EMPIRICAL COMPARISON; EXPERIMENTAL EVALUATION; MARGIN THEORY; THEORETICAL EXPLANATION;

EID: 67549150807     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DICTA.2008.47     Document Type: Conference Paper
Times cited : (16)

References (13)
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    • Freund, Y.1    Schapire, R.E.2
  • 5
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    • Additive logistic regression: A statistical view of boosting with discussion and a rejoinder by the authors
    • J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Statist., 28(2):337-407, 2000.
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    • FloatBoost learning and statistical face detection
    • S. Z. Li and Z. Zhang. FloatBoost learning and statistical face detection. IEEE Trans. Pattern Anal. Mach. Intell., 26(9):1112-1123, 2004.
    • (2004) IEEE Trans. Pattern Anal. Mach. Intell , vol.26 , Issue.9 , pp. 1112-1123
    • Li, S.Z.1    Zhang, Z.2
  • 9
    • 0342502195 scopus 로고    scopus 로고
    • Soft margins for AdaBoost
    • DOI 10.1023/A:1007618119488
    • G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for AdaBoost. Mach. Learn., 42(3):287-320, 2001. data sets are available at http://theoval.cmp.uea.ac.uk/ ∼gcc/matlab/index.shtml. (Pubitemid 32188795)
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    • Ratsch, G.1    Onoda, T.2    Muller, K.-R.3
  • 10
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    • Efficient margin maximizing with boosting
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    • How boosting the margin can also boost classifier complexity.
    • Pittsburgh, Pennsylvania, USA
    • 11] L. Reyzin and R. E. Schapire. How boosting the margin can also boost classifier complexity. In Proc. Int. Conf. Mach. Learn., Pittsburgh, Pennsylvania, USA, 2006.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.