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Volumn 2534, Issue , 2002, Pages 165-177

From ensemble methods to comprehensible models

Author keywords

Classifier similarity; Comprehensibility in machine learning; Decision trees; Ensemble methods; Randomisation

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

EID: 84949787320     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36182-0_16     Document Type: Conference Paper
Times cited : (18)

References (22)
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  • 3
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    • A coefficient of agreement for nominal scales
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  • 5
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    • Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees
    • Thomas G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, Boosting, and Randomization. Machine Learning, 40(2):139–157, 2000.
    • (2000) Bagging, Boosting, and Randomization. Machine Learning , vol.40 , Issue.2 , pp. 139-157
    • Thomas, G.1
  • 19
    • 33748276324 scopus 로고    scopus 로고
    • Relational learning and boosting
    • Saso Dzeroski and Nada Lavrac, editors, Springer-Verlag, September
    • Ross Quinlan. Relational learning and boosting. In Saso Dzeroski and Nada Lavrac, editors, Relational Data Mining, pages 292–306. Springer-Verlag, September 2001.
    • (2001) Relational Data Mining , pp. 292-306
    • Quinlan, R.1
  • 21
    • 0029679031 scopus 로고    scopus 로고
    • Further experimental evidence against the utility of Occam’s razor
    • Geoffrey I. Webb. Further experimental evidence against the utility of Occam’s razor. Journal of Artificial Intelligence Research, 4:397–417, 1996.
    • (1996) Journal of Artificial Intelligence Research , vol.4 , pp. 397-417
    • Webb, G.I.1
  • 22
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    • Stacked generalization
    • David H. Wolpert. Stacked generalization. Neural Networks, 5(2):241–259, 1992.
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    • Wolpert, D.H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.