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Volumn , Issue , 2007, Pages 102-106

Online incremental random forests

Author keywords

Ensemble learning; Feature selection; Random forests

Indexed keywords

BATCH LEARNING; BATCH MODES; DATA-SETS; ENSEMBLE LEARNING; FEATURE SELECTION; FEATURES SELECTION; GAIN RATIOS; GINI INDEX; IMPORTANCE OF VARIABLES; INTERNATIONAL CONFERENCES; MACHINE VISION; ON-LINE METHODS; RANDOM FORESTS; RELIEFF;

EID: 49649115511     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMV.2007.4469281     Document Type: Conference Paper
Times cited : (9)

References (13)
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  • 2
    • 0035478854 scopus 로고    scopus 로고
    • Leo Breiman, Random Forests, Machine Learning, 45(1):5.32, 2001.
    • Leo Breiman, "Random Forests," Machine Learning, 45(1):5.32, 2001.
  • 4
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • R. Schapire, Y. Freund, R Bartlett, and W. Lee, "Boosting the margin: a new explanation for the effectiveness of voting methods," Ann. Statist, 26(5):1651-1686, 1998.
    • (1998) Ann. Statist , vol.26 , Issue.5 , pp. 1651-1686
    • Schapire, R.1    Freund, Y.2    Bartlett, R.3    Lee, W.4
  • 5
    • 33750357665 scopus 로고    scopus 로고
    • Improving Random Forests
    • J.F. Boulicaut et al.eds, Springer, Berlin
    • Marko Robnik-Sikonja, "Improving Random Forests," In J.F. Boulicaut et al.(eds): Machine Learning, ECML 2004 Proceedings, Springer, Berlin, 2004.
    • (2004) Machine Learning, ECML 2004 Proceedings
    • Robnik-Sikonja, M.1
  • 7
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artifcial Intelligence, 97(1-2):273-324, 1997.
    • (1997) Artifcial Intelligence , vol.97 , Issue.1-2 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 9
    • 84992726552 scopus 로고
    • and, editors, Machine Learning: ECML-94, Springer Verlag, Berlin
    • Igor Kononenko, "Estimating attributes: analysis and extensions of Relief," In Luc De Raedt and Francesco Bergadano, editors, Machine Learning: ECML-94, pages 171-182. Springer Verlag, Berlin, 1994.
    • (1994) Estimating attributes: Analysis and extensions of Relief , pp. 171-182
    • Kononenko, I.1
  • 10
    • 49649113285 scopus 로고    scopus 로고
    • MA. Hall, L.A. Smith, Practical feature subset selection for machine learning, In Proceedings of the 21st Australian Compute Science Conference,1998, pp. 181.191
    • MA. Hall, L.A. Smith, "Practical feature subset selection for machine learning", In Proceedings of the 21st Australian Compute Science Conference,1998, pp. 181.191
  • 12
    • 0013161560 scopus 로고    scopus 로고
    • On feature selection: Learning with exponentially many irrelevant features as training examples
    • Ng, A. Y, "On feature selection: learning with exponentially many irrelevant features as training examples," Proceedings of the Fifteenth International Conference on Machine Learning (pp. 404-412), 1998.
    • (1998) Proceedings of the Fifteenth International Conference on Machine Learning , pp. 404-412
    • Ng, A.Y.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.