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Volumn , Issue , 2009, Pages 880-888

Potential-based agnostic boosting

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; POLYNOMIAL APPROXIMATION;

EID: 84858723486     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (53)

References (26)
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    • Haussler, D.1
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    • 0001553979 scopus 로고
    • Toward efficient agnostic learning
    • M. Kearns, R. Schapire, and L. Sellie. Toward Efficient Agnostic Learning. Machine Learning, 17(2):115-141, 1994.
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    • Random classification noise defeats all convex potential boosters
    • P. M. Long and R. A. Servedio. Random classification noise defeats all convex potential boosters. In ICML, pages 608-615, 2008.
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    • Long, P.M.1    Servedio, R.A.2
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    • J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28:2000, 1998.
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    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
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    • Using validation sets to avoid overfitting in adaboost
    • G. Sutcliffe and R. Goebel, editors AAAI Press
    • T. Bylander and L. Tate. Using validation sets to avoid overfitting in adaboost. In G. Sutcliffe and R. Goebel, editors, FLAIRS Conference, pages 544-549. AAAI Press, 2006.
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    • Irvine, CA: University of California, School of Information and Computer Science
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.