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Volumn 1910, Issue , 2000, Pages 136-147

Some enhancements of decision tree bagging

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; LARGE DATASET;

EID: 84974731014     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45372-5_14     Document Type: Conference Paper
Times cited : (13)

References (13)
  • 1
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36:105-139, 1999.
    • (1999) Machine Learning , vol.36 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 2
    • 0003495934 scopus 로고
    • Technical report, University of California, Department of Statistics, September
    • L. Breiman. Bagging predictors. Technical report, University of California, Department of Statistics, September 1994.
    • (1994) Bagging predictors
    • Breiman, L.1
  • 3
    • 0032634129 scopus 로고    scopus 로고
    • Pasting small votes for classification in large databases and on-line
    • L. Breiman. Pasting small votes for classification in large databases and on-line. Machine Learning, 36:85-103, 1999.
    • (1999) Machine Learning , vol.36 , pp. 85-103
    • Breiman, L.1
  • 4
    • 0003856278 scopus 로고    scopus 로고
    • Technical report, Statistics Department, University of California, Berkeley, February
    • L. Breiman. Using adaptive bagging to debias regressions. Technical report, Statistics Department, University of California, Berkeley, February 1999.
    • (1999) Using adaptive bagging to debias regressions
    • Breiman, L.1
  • 6
    • 0013281807 scopus 로고    scopus 로고
    • Technical report, Statistics Department, Standford University, January
    • J. H. Friedman and P. Hall. On bagging and nonlinear estimation. Technical report, Statistics Department, Standford University, January 2000.
    • (2000) On bagging and nonlinear estimation
    • Friedman, J.H.1    Hall, P.2
  • 7
    • 21744462998 scopus 로고    scopus 로고
    • On bias, variance, 0/1-loss, and the curse-of-dimensionality
    • J.H. Friedman. On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1:55-77, 1997.
    • (1997) Data Mining and Knowledge Discovery , vol.1 , pp. 55-77
    • Friedman, J.H.1
  • 12
    • 85127438349 scopus 로고    scopus 로고
    • Learning with ensembles: How over-fitting can be useful
    • D.S. Touretzky, M.C. Mozer, and M.E. Hasselmo, editors, MIT Press
    • Peter Sollich and Anders Krogh. Learning with ensembles: How over-fitting can be useful. In D.S. Touretzky, M.C. Mozer, and M.E. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8, pages 190-196. MIT Press, 1996.
    • (1996) Advances in Neural Information Processing Systems , vol.8 , pp. 190-196
    • Sollich, P.1    Krogh, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.