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Volumn 1857 LNCS, Issue , 2000, Pages 87-96

Some results on weakly accurate base learners for boosting regression and classification

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS; ALGORITHMS;

EID: 84867069706     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45014-9_8     Document Type: Conference Paper
Times cited : (4)

References (16)
  • 2
    • 0003929807 scopus 로고    scopus 로고
    • Prediction games and arcing classifiers
    • University of California at Berkeley, 1997
    • BREIMAN, L. (1997a). Prediction games and arcing classifiers. Technical Report 504, Statistics Department, University of California at Berkeley, 1997.
    • (1997) Technical Report 504, Statistics Department
    • Breiman, L.1
  • 4
    • 58149321460 scopus 로고
    • Boosting a weak learning algorithm by majority
    • FREUND, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121 256-285.
    • (1995) Information and Computation , vol.121 , pp. 256-285
    • Freund, Y.1
  • 6
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • FREUND, Y. and SCHAPIRE, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55 119-139.
    • (1997) Journal of Computer and System Sciences , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 7
    • 0003591748 scopus 로고    scopus 로고
    • Greedy function approximation: A gradient boosting machine
    • Stanford University
    • FRIEDMAN, J. H. (1999). Greedy function approximation: a gradient boosting machine. Technical Report, Department of Statistics, Stanford University.
    • (1999) Technical Report, Department of Statistics
    • Friedman, J.H.1
  • 9
  • 11
    • 84867083116 scopus 로고    scopus 로고
    • Large time behavior of boosting algorithms for regression and classification
    • Northwestern University
    • JIANG, W. (1999). Large time behavior of boosting algorithms for regression and classification. Technical Report, Department of Statistics, Northwestern University.
    • (1999) Technical Report, Department of Statistics
    • Jiang, W.1
  • 12
    • 84867094215 scopus 로고    scopus 로고
    • On weak base learners for boosting regression and classification
    • Northwestern University
    • JIANG, W. (2000). On weak base learners for boosting regression and classification. Technical Report, Department of Statistics, Northwestern University.
    • (2000) Technical Report, Department of Statistics
    • Jiang, W.1
  • 14
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • SCHAPIRE, R. E. (1990). The strength of weak learnability. Machine Learning, 5 197-227.
    • (1990) Machine Learning , vol.5 , pp. 197-227
    • Schapire, R.E.1
  • 16
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • SCHAPIRE, R. E., FREUND, Y., BARTLETT, P. AND LEE, W. S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26 1651-1686.
    • (1998) The Annals of Statistics , vol.26 , pp. 1651-1686
    • Schapire, R.E.1    Freund, Y.2    Bartlett, P.3    Lee, W.S.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.