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Volumn 2709, Issue , 2003, Pages 276-285

Spectral coefficients and classifier correlation

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTERS;

EID: 35248840118     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44938-8_28     Document Type: Article
Times cited : (1)

References (14)
  • 2
    • 0346786584 scopus 로고    scopus 로고
    • Arcing classifiers
    • L. Breiman. Arcing classifiers. The Annals of Statistics, 26(3):801-849, 1998.
    • (1998) The Annals of Statistics , vol.26 , Issue.3 , pp. 801-849
    • Breiman, L.1
  • 4
    • 0037403462 scopus 로고    scopus 로고
    • Variance and bias for general loss functions
    • to appear
    • G. M. James. Variance and bias for general loss functions. Machine Learning, 2003. to appear.
    • (2003) Machine Learning
    • James, G.M.1
  • 6
    • 0036896235 scopus 로고    scopus 로고
    • An experimental study on diversity for bagging and boosting with linear classifiers
    • L.I. Kuncheva, M. Skurichina, and R.P.W. Duin. An experimental study on diversity for bagging and boosting with linear classifiers. Information Fusion, 3(4):245-258, 2002.
    • (2002) Information Fusion , vol.3 , Issue.4 , pp. 245-258
    • Kuncheva, L.I.1    Skurichina, M.2    Duin, R.P.W.3
  • 7
    • 0032661927 scopus 로고    scopus 로고
    • Using correspondence analysis to combine classifiers
    • C. J. Merz. Using correspondence analysis to combine classifiers. Machine Learning, 36(1-2):33-58, 1999.
    • (1999) Machine Learning , vol.36 , Issue.1-2 , pp. 33-58
    • Merz, C.J.1
  • 10
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • R. E. Schapire, Y. Freund, and P. Bartlett. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, 26(5):1651-1686, 1998.
    • (1998) Annals of Statistics , vol.26 , Issue.5 , pp. 1651-1686
    • Schapire, R.E.1    Freund, Y.2    Bartlett, P.3
  • 11
    • 0035363682 scopus 로고    scopus 로고
    • Recursive partitioning technique for combining multiple classifiers
    • T. Windeatt. Recursive partitioning technique for combining multiple classifiers. Neural Processing Letters, 13(3):221-236, 2001.
    • (2001) Neural Processing Letters , vol.13 , Issue.3 , pp. 221-236
    • Windeatt, T.1
  • 12
    • 84947560755 scopus 로고    scopus 로고
    • Boosted tree ensembles for solving multiclass problems
    • F. Roli and J. Kittler, editors, 3rd Int. Workshop Multiple Classifier Systems, Springer-Verlag
    • T. Windeatt and G. Ardeshir. Boosted tree ensembles for solving multiclass problems. In F. Roli and J. Kittler, editors, 3rd Int. Workshop Multiple Classifier Systems, pages 42-51. Springer-Verlag, Lecture Notes in Computer Science, 2002.
    • (2002) Lecture Notes in Computer Science , pp. 42-51
    • Windeatt, T.1    Ardeshir, G.2
  • 13
    • 0001652061 scopus 로고    scopus 로고
    • Binary labelling and decision level fusion
    • T. Windeatt and R. Ghaderi. Binary labelling and decision level fusion. Information Fusion, 2(2):103-112, 2001.
    • (2001) Information Fusion , vol.2 , Issue.2 , pp. 103-112
    • Windeatt, T.1    Ghaderi, R.2
  • 14
    • 0031198065 scopus 로고    scopus 로고
    • Spectral technique for hidden layer neural network training
    • T. Windeatt and R. Tebbs. Spectral technique for hidden layer neural network training. Pattern Recognition Letters, 18(8):723-731, 1997.
    • (1997) Pattern Recognition Letters , vol.18 , Issue.8 , pp. 723-731
    • Windeatt, T.1    Tebbs, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.