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Volumn 3077, Issue , 2004, Pages 31-40

AveBoost2: Boosting for noisy data

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ERRORS;

EID: 33748611698     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-25966-4_3     Document Type: Article
Times cited : (36)

References (8)
  • 2
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman. Bagging predictors. Machine Learning, 24(2): 123-140, 1996.
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 3
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • Aug.
    • Thomas G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40:139-158, Aug. 2000.
    • (2000) Machine Learning , vol.40 , pp. 139-158
    • Dietterich, T.G.1
  • 6
    • 0012302736 scopus 로고    scopus 로고
    • The interaction of stability and weakness in ad-aboost
    • University of Chicago, October
    • Samuel Kutin and Partha Niyogi. The interaction of stability and weakness in ad-aboost. Technical Report TR-2001-30, University of Chicago, October 2001.
    • (2001) Technical Report TR-2001-30
    • Kutin, S.1    Niyogi, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.