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Volumn , Issue , 2010, Pages

A Bayesian approach to concept drift

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS;

EID: 85161980756     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (23)

References (17)
  • 3
    • 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
  • 5
    • 37749050180 scopus 로고    scopus 로고
    • Dynamic weighted majority: An ensemble method for drifting concepts
    • Dec
    • J. Z. Kolter and M. A. Maloof. Dynamic weighted majority: An ensemble method for drifting concepts. Journal of Machine Learning Research, 8:2755-2790, Dec 2007.
    • (2007) Journal of Machine Learning Research , vol.8 , pp. 2755-2790
    • Kolter, J.Z.1    Maloof, M.A.2
  • 11
    • 0001713234 scopus 로고
    • Product partition models for change point problems
    • D. Barry and J. A. Hartigan. Product partition models for change point problems. The Annals of Statistics, 20(1):260-279, 1992.
    • (1992) The Annals of Statistics , vol.20 , Issue.1 , pp. 260-279
    • Barry, D.1    Hartigan, J.A.2
  • 12
    • 33646687960 scopus 로고    scopus 로고
    • Exact and efficient Bayesian inference for multiple changepoint problems
    • Paul Fearnhead. Exact and efficient Bayesian inference for multiple changepoint problems. Statistics and Computing, 16(2):203-213, 2006.
    • (2006) Statistics and Computing , vol.16 , Issue.2 , pp. 203-213
    • Fearnhead, P.1
  • 15
    • 0030819669 scopus 로고    scopus 로고
    • Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain
    • A. Blum. Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain. Machine Learning, 26:5-23, 1997.
    • (1997) Machine Learning , vol.26 , pp. 5-23
    • Blum, A.1
  • 17


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.