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Volumn , Issue , 2008, Pages 33-42

Predicting future decision trees from evolving data

Author keywords

[No Author keywords available]

Indexed keywords

CHANGE MINING; MODEL CHANGE; MODEL LEARNING;

EID: 67049119059     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2008.90     Document Type: Conference Paper
Times cited : (10)

References (22)
  • 2
    • 0016355478 scopus 로고
    • A new look at the statistical model identification
    • H. Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716-723, 1974.
    • (1974) IEEE Transactions on Automatic Control , vol.19 , Issue.6 , pp. 716-723
    • Akaike, H.1
  • 3
    • 18444373554 scopus 로고    scopus 로고
    • A survey on tree edit distance and related problems
    • P. Bille. A survey on tree edit distance and related problems. Theoretical Computer Science, 337(1-3):217-239, 2005.
    • (2005) Theoretical Computer Science , vol.337 , Issue.1-3 , pp. 217-239
    • Bille, P.1
  • 5
    • 0030344230 scopus 로고    scopus 로고
    • The heuristics of instability in model selection
    • L. Breiman. The heuristics of instability in model selection. Annals of Statistics, 24:2350-2383, 1996.
    • (1996) Annals of Statistics , vol.24 , pp. 2350-2383
    • Breiman, L.1
  • 7
    • 8744307994 scopus 로고    scopus 로고
    • Multimodel inference: Understanding AIC and BIC in model selection
    • DOI 10.1177/0049124104268644
    • K. P. Burnham and D. R. Anderson. Multimodel inference: understanding AIC and BIC inmodel selection. Sociological Methods & Research, 33:261-304, 2004. (Pubitemid 39519124)
    • (2004) Sociological Methods and Research , vol.33 , Issue.2 , pp. 261-304
    • Burnham, K.P.1    Anderson, D.R.2
  • 8
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • J. Demš;ar. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7:1-30, 2006. (Pubitemid 43022939)
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demsar, J.1
  • 11
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • DOI 10.1016/0893-6080(89)90020-8
    • K. Hornik, M. Stinchcombe, and H.White. Multilayer feedforward networks are universal approximators. Neural Net- works, 2(5):359-366, 1989. (Pubitemid 20609008)
    • (1989) Neural Networks , vol.2 , Issue.5 , pp. 359-366
    • Hornik Kurt1    Stinchcombe Maxwell2    White Halbert3
  • 13
    • 70349119250 scopus 로고
    • Regression and time series model selection in small samples
    • C. M. Hurvich and C. L. Tsai. Regression and time series model selection in small samples. Biometrika, 76:297-307, 1989.
    • (1989) Biometrika , vol.76 , pp. 297-307
    • Hurvich, C.M.1    Tsai, C.L.2
  • 15
    • 84883713774 scopus 로고    scopus 로고
    • Learning drifting concepts: Example selection vs. example weighting
    • R. Klinkenberg. Learning drifting concepts: Example selection vs. example weighting. Intelligent Data Analysis, 8(3):281-300, 2004.
    • (2004) Intelligent Data Analysis , vol.8 , Issue.3 , pp. 281-300
    • Klinkenberg, R.1
  • 16
    • 78149338936 scopus 로고    scopus 로고
    • Analyzing the interestingness of association rules from the temporal dimension
    • San Jose, CA
    • B. Liu, Y. Ma, and R. Lee. Analyzing the interestingness of association rules from the temporal dimension. In Proceed- ings of the IEEE International Conference on Data Mining, pages 377-384, San Jose, CA, 2001.
    • (2001) Proceedings of the IEEE International Conference on Data Mining , pp. 377-384
    • Liu, B.1    Ma, Y.2    Lee, R.3
  • 19
    • 33744584654 scopus 로고    scopus 로고
    • Induction of decision trees
    • J. R. Quinlan. Induction of decision trees. Machine Learn- ing, 1(1):81-106, 1996.
    • (1996) Machine Learn-ing , vol.1 , Issue.1 , pp. 81-106
    • Quinlan, J.R.1
  • 21
    • 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(1):69-101, 1996. (Pubitemid 126737384)
    • (1996) Machine Learning , vol.23 , Issue.1 , pp. 69-101
    • Widmer, G.1
  • 22
    • 32344442287 scopus 로고    scopus 로고
    • Combining proactive and reactive predictions for data streams
    • DOI 10.1145/1081870.1081961, KDD-2005 - Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    • Y. Yang, X. Wu, and X. Zhu. Combining proactive and reactive predictions for data streams. In Proceeding of the 11th ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining (KDD'05), pages 710- 715, New York, NY, USA, 2005. ACM Press. (Pubitemid 43218344)
    • (2005) Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pp. 710-715
    • Yang, Y.1    Wu, X.2    Zhu, X.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.