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Volumn , Issue , 2007, Pages 2645-2650

Distributed data mining: Why do more than aggregating models

Author keywords

[No Author keywords available]

Indexed keywords

AGGREGATED MODELS; CLASSIFICATION RULES; DISTRIBUTED DATA MINING; DISTRIBUTED DATABASE; MAJORITY VOTE; PREDICTION CAPABILITY;

EID: 70349807940     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (6)

References (26)
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    • (1996) Advances in Knowledge Discovery and Data Mining , pp. 471-493
    • Fayyad, U.M.1    Djorgovski, S.G.2    Weir, N.3
  • 12
    • 21844462365 scopus 로고    scopus 로고
    • Tutorial on practical prediction theory for classification
    • March
    • John Langford. Tutorial on practical prediction theory for classification. Journal of Machine Learning Research, (6):273-306, March 2005.
    • (2005) Journal of Machine Learning Research , Issue.6 , pp. 273-306
    • Langford, J.1
  • 14
    • 0002039637 scopus 로고
    • Cancer diagnosis via linear programming
    • September
    • O. L. Mangasarian and W. H. Wolberg. Cancer diagnosis via linear programming. SIAM News, 23(5):1-18, September 1990.
    • (1990) SIAM News , vol.23 , Issue.5 , pp. 1-18
    • Mangasarian, O.L.1    Wolberg, W.H.2
  • 15
    • 0002522158 scopus 로고    scopus 로고
    • Meta-learning in distributed data mining systems: Issues and approaches
    • Hillol Kargupta and Philip Chan, editors, AAAI Press / MIT Press, Menlo Park, CA / Cambridge, MA, chap. 3
    • Andreas L. Prodromidis, Philip K. Chan, and Salvatore J. Stolfo. Meta-learning in distributed data mining systems: Issues and approaches. In Hillol Kargupta and Philip Chan, editors, Advances in Distributed and Parallel Knowledge Discovery, pages 81-113. AAAI Press / MIT Press, Menlo Park, CA / Cambridge, MA, 2000. chap. 3 part II.
    • (2000) Advances in Distributed and Parallel Knowledge Discovery , Issue.PART II , pp. 81-113
    • Prodromidis, A.L.1    Chan, P.K.2    Stolfo, S.J.3
  • 20
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • Robert E. 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.E.1
  • 22
    • 0030489415 scopus 로고    scopus 로고
    • A Computational Study of Distributed Rule Learning
    • June
    • Riyaz Sikora and Michael Shaw. A Computational Study of Distributed Rule Learning. Information Systems Research, 7(2):189-197, June 1996.
    • (1996) Information Systems Research , vol.7 , Issue.2 , pp. 189-197
    • Sikora, R.1    Shaw, M.2
  • 24
    • 0000835392 scopus 로고
    • OPUS: An efficient admissible algorithm for unordered search
    • Geoffrey I. Webb. OPUS: An efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research, 3:431-465, 1995.
    • (1995) Journal of Artificial Intelligence Research , vol.3 , pp. 431-465
    • Webb, G.I.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.