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Volumn 26, Issue 3, 1998, Pages 801-849

Arcing classifiers

Author keywords

Bagging; Boosting; Decision trees; Ensemble methods; Error correcting; Markov chain; Monte Carlo; Neural networks; Output coding

Indexed keywords


EID: 0346786584     PISSN: 00905364     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (1144)

References (21)
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    • Bagging predictors
    • BREIMAN, L. (1996a). Bagging predictors. Machine Learning 26 123-140.
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    • Breiman, L.1
  • 3
    • 0030344230 scopus 로고    scopus 로고
    • The heuristics of instability in model selection
    • BREIMAN, L. (1996b). The heuristics of instability in model selection. Ann. Statist. 24 2350-2383.
    • (1996) Ann. Statist. , vol.24 , pp. 2350-2383
    • Breiman, L.1
  • 7
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • FREUND, Y. and SCHAPIRE, R. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. System Sci. 55 119-139.
    • (1997) J. Comput. System Sci. , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.2
  • 11
    • 0004175767 scopus 로고
    • Learning Boolean formulae or finite automata is as hard as factoring
    • Aiken Computation Laboratory, Harvard Univ.
    • KEARNS, M. and VALIANT, L. G. (1988). Learning Boolean formulae or finite automata is as hard as factoring. Technical Report TR-14-88, Aiken Computation Laboratory, Harvard Univ.
    • (1988) Technical Report TR-14-88
    • Kearns, M.1    Valiant, L.G.2
  • 14
    • 84992322729 scopus 로고
    • Error-correcting output coding corrects bias and variance
    • (A. Prieditis and S. Russell, eds.) Morgan Kaufmann, San Francisco
    • KONG, E. B. and DIETTERICH, T. G. (1995). Error-correcting output coding corrects bias and variance. In Proceedings of the Twelfth International Conference on Machine Learning (A. Prieditis and S. Russell, eds.) 313-321. Morgan Kaufmann, San Francisco.
    • (1995) Proceedings of the Twelfth International Conference on Machine Learning , pp. 313-321
    • Kong, E.B.1    Dietterich, T.G.2
  • 18
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • SCHAPIRE, R. (1990). The strength of weak learnability. Machine Learning 5 197-227.
    • (1990) Machine Learning , vol.5 , pp. 197-227
    • Schapire, R.1
  • 20
    • 0003667773 scopus 로고    scopus 로고
    • Bias, variance, and prediction error for classification rules
    • Dept. Statistics, Univ. Toronto
    • TIBSHIRANI, R. (1996). Bias, variance, and prediction error for classification rules. Technical Report, Dept. Statistics, Univ. Toronto.
    • (1996) Technical Report
    • Tibshirani, R.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.