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Volumn 3721 LNAI, Issue , 2005, Pages 84-95

Ensembles of balanced nested dichotomies for multi-class problems

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBABILITY; PROBLEM SOLVING; RANDOM PROCESSES; TREES (MATHEMATICS);

EID: 33646395091     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564126_13     Document Type: Conference Paper
Times cited : (67)

References (9)
  • 1
    • 14344258243 scopus 로고    scopus 로고
    • Ensembles of nested dichotomies for multi-class problems
    • ACM Press
    • Frank, E., Kramer, S.: Ensembles of nested dichotomies for multi-class problems. In: Proc Int Conf on Machine Learning, ACM Press (2004) 305-312
    • (2004) Proc Int Conf on Machine Learning , pp. 305-312
    • Frank, E.1    Kramer, S.2
  • 2
    • 0000406788 scopus 로고
    • Solving multiclass learning problems via error-correcting output codes
    • Dietterich, T., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2 (1995) 263-286
    • (1995) Journal of Artificial Intelligence Research , vol.2 , pp. 263-286
    • Dietterich, T.1    Bakiri, G.2
  • 5
    • 0003408496 scopus 로고    scopus 로고
    • University of California, Irvine, Dept. of Inf. and Computer Science
    • Blake, C., Merz, C.: UCI repository of machine learning databases. University of California, Irvine, Dept. of Inf. and Computer Science (1998) [www.ics.uci.edu/~mlearn/MLRepository.html].
    • (1998) UCI Repository of Machine Learning Databases
    • Blake, C.1    Merz, C.2
  • 8
    • 0042847140 scopus 로고    scopus 로고
    • Inference for the generalization error
    • Nadeau, C., Bengio, Y.: Inference for the generalization error. Machine Learning 52 (2003) 239-281
    • (2003) Machine Learning , vol.52 , pp. 239-281
    • Nadeau, C.1    Bengio, Y.2
  • 9
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40 (1998) 139-157
    • (1998) Machine Learning , vol.40 , pp. 139-157
    • Dietterich, T.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.