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Volumn 1, Issue , 2010, Pages 359-363

A new Diverse AdaBoost classifier

Author keywords

AdaBoost; Classifier; Diversity; Ensemble

Indexed keywords

ADABOOST; ADABOOST ALGORITHM; DIVERSITY; ENSEMBLE; ENSEMBLE CLASSIFIERS; ERROR MINIMIZATION; GENTLE ADABOOST ALGORITHM; ITERATION STEP; LINEAR COMBINATIONS; TRAINING PROCESS;

EID: 78651458680     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AICI.2010.82     Document Type: Conference Paper
Times cited : (121)

References (11)
  • 1
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    • R. E. Schapire and Y. Singer, "Improved Boosting Algorithms Using Confidence-rated Predictions" Machine Learning, vol. 37, No. 3, pp. 297-336, December 1999.
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    • Schapire, R.E.1    Singer, Y.2
  • 2
    • 10444259853 scopus 로고    scopus 로고
    • Creating diversity in ensembles using artificial data
    • March
    • P. Melville and R. J. Mooney, "Creating diversity in ensembles using artificial data", Information Fusion. vol. 6, No. 1, pp. 99-111, March 2005.
    • (2005) Information Fusion , vol.6 , Issue.1 , pp. 99-111
    • Melville, P.1    Mooney, R.J.2
  • 4
    • 0037403516 scopus 로고    scopus 로고
    • Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
    • May
    • L. I. Kuncheva and C. J. Whitaker, "Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy", Machine Learning, vol. 51, no. 2, pp. 181-207, May 2003.
    • (2003) Machine Learning , vol.51 , Issue.2 , pp. 181-207
    • Kuncheva, L.I.1    Whitaker, C.J.2
  • 5
    • 84867038166 scopus 로고    scopus 로고
    • Classifier combinations: Implementations and theoretical issues
    • Multiple Classifier Systems. Cagliari, Italy
    • L. Lam, "Classifier Combinations: Implementations and Theoretical Issues", Multiple Classifier Systems, vol. 1857 of Lecture Notes in Computer Science. Cagliari, Italy, pp. 78-86. 2000.
    • (2000) Lecture Notes in Computer Science , vol.1857 , pp. 78-86
    • Lam, L.1
  • 7
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • August
    • T. G. Dietterich, "An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization", Machine Learning, vol. 40, No. 2, pp. 139-157, August 2000.
    • (2000) Machine Learning , vol.40 , Issue.2 , pp. 139-157
    • Dietterich, T.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.