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Volumn , Issue , 2001, Pages 377-382

A streaming ensemble algorithm (SEA) for large-scale classification

Author keywords

Ensemble classification; Streaming data

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; DATABASE SYSTEMS; DECISION THEORY; HEURISTIC METHODS; PROBLEM SOLVING; TREES (MATHEMATICS);

EID: 0035788947     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (1113)

References (22)
  • 1
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • July-August
    • E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36:105-142, July-August 1999.
    • (1999) Machine Learning , vol.36 , pp. 105-142
    • Bauer, E.1    Kohavi, R.2
  • 2
    • 0003408496 scopus 로고    scopus 로고
    • University of California, Irvine, Department of Information and Computer Sciences.
    • C. L. Blake and C. J. Merz. UCI repository of machine learning databases [http://www.ics.uci.edu/̃mlearn/MLRepository.html], 1998. University of California, Irvine, Department of Information and Computer Sciences.
    • (1998) UCI Repository of Machine Learning Databases
    • Blake, C.L.1    Merz, C.J.2
  • 4
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 5
    • 0346786584 scopus 로고    scopus 로고
    • Arcing classifiers
    • L. Breiman. Arcing classifiers. Annals of Statistics, 26(3):801-849, 1998.
    • (1998) Annals of Statistics , vol.26 , Issue.3 , pp. 801-849
    • Breiman, L.1
  • 6
    • 0024533068 scopus 로고
    • Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases
    • C. L. Carter, C. Allen, and D. E. Henson. Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer, 63:181-187, 1989.
    • (1989) Cancer , vol.63 , pp. 181-187
    • Carter, C.L.1    Allen, C.2    Henson, D.E.3
  • 14
    • 0029372769 scopus 로고
    • Methods for combining experts' probability assessments
    • R. A. Jacobs. Methods for combining experts' probability assessments. Neural Computation, 7:867-888, 1995.
    • (1995) Neural Computation , vol.7 , pp. 867-888
    • Jacobs, R.A.1
  • 20
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • R. Schapire. The strength of weak learnability. Machine Learning, 5(2):197-227, 1990.
    • (1990) Machine Learning , vol.5 , Issue.2 , pp. 197-227
    • Schapire, R.1
  • 21
    • 0000524365 scopus 로고    scopus 로고
    • Learning with ensembles: How overfitting can be useful
    • D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors. MIT Press
    • P. Sollich and A. Krogh. Learning with ensembles: How overfitting can be useful. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8. MIT Press, 1996.
    • (1996) Advances in Neural Information Processing Systems , vol.8
    • Sollich, P.1    Krogh, A.2
  • 22
    • 0030126609 scopus 로고    scopus 로고
    • Learning in the presence of concept drift and hidden contexts
    • G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine Learning, 23:69-101, 1996.
    • (1996) Machine Learning , vol.23 , pp. 69-101
    • Widmer, G.1    Kubat, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.