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Volumn 2364, Issue , 2002, Pages 42-51

Boosted tree ensembles for solving multiclass problems

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BOOSTING;

EID: 84947560755     PISSN: 03029743     EISSN: 16113349     Source Type: Journal    
DOI: 10.1007/3-540-45428-4_4     Document Type: Article
Times cited : (12)

References (25)
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  • 8
    • 84947553303 scopus 로고    scopus 로고
    • On the learnability and design of output codes for multiclass problems
    • to appear
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    • Machine Learning
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    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
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    • (2000) Machine Learning , vol.40 , Issue.2 , pp. 139-158
    • Ietterich, T.G.D.1
  • 12
    • 0000406788 scopus 로고
    • Solving multi-class learning problems via errorcorrecting output codes
    • [12] T.G. Dietterich and G Bakiri. Solving multi-class learning problems via errorcorrecting output codes. Journal of Artificial Intelligence Research, 2:263-286, 1995.
    • (1995) Journal of Artificial Intelligence Research , vol.2 , pp. 263-286
    • Dietterich, T.G.1    Bakiri, G.2
  • 17
    • 33744584654 scopus 로고
    • Induction of decision tree
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    • (1986) Machine Learning , vol.1 , pp. 81-106
    • Quinlan, R.1
  • 20
    • 0000040021 scopus 로고    scopus 로고
    • Using output codes to boost multiclass learning problems
    • Morgan Kaufman
    • [20] R.E. Schapire. Using output codes to boost multiclass learning problems. In 14th International Conf. on Machine Learning, pages 313-321.Morgan Kaufman, 1997.
    • (1997) Th International Conf. On Machine Learning , pp. 313-321
    • Schapire, R.E.1
  • 23
    • 58549098738 scopus 로고    scopus 로고
    • An empirical comparison of pruning methods for ensemble classifiers
    • Spri nger-Verlag, Lecture notes in computer science
    • [23] T. Windeatt and G. Ardeshir. An empirical comparison of pruning methods for ensemble classifiers.In IDA 2001.Spri nger-Verlag, Lecture notes in computer science, 2001.
    • (2001) IDA 2001
    • Windeatt, T.1    Ardeshir, G.2
  • 24
    • 0034648563 scopus 로고    scopus 로고
    • Multi-class learning and error-correcting code sensitivity
    • [24] T. Windeatt and R. Ghaderi. Multi-class learning and error-correcting code sensitivity. Electronics Letters, 36(19):1630-1632, Sep 2000.
    • (2000) Electronics Letters , vol.36 , Issue.19 , pp. 1630-1632
    • Windeatt, T.1    Ghaderi, R.2
  • 25
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    • Binary labelling and decision level fusion
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    • Windeatt, T.1    Ghaderi, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.