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Volumn , Issue , 2002, Pages 525-531

Mining complex models from arbitrarily large databases in constant time

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; ELECTRONIC COMMERCE; LEARNING ALGORITHMS; PATTERN RECOGNITION;

EID: 0242540431     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/775107.775124     Document Type: Conference Paper
Times cited : (43)

References (17)
  • 1
    • 0028424239 scopus 로고
    • Improving generalization with active learning
    • D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 15:201-221, 1994.
    • (1994) Machine Learning , vol.15 , pp. 201-221
    • Cohn, D.1    Atlas, L.2    Ladner, R.3
  • 2
    • 0036102015 scopus 로고    scopus 로고
    • Adaptive sampling methods for scaling up knowledge discovery algorithms
    • C. Domingo, R. Gavalda, and O. Watanabe. Adaptive sampling methods for scaling up knowledge discovery algorithms. Data Mining and Knowledge Discovery, 6:131-152, 2002.
    • (2002) Data Mining and Knowledge Discovery , vol.6 , pp. 131-152
    • Domingo, C.1    Gavalda, R.2    Watanabe, O.3
  • 5
    • 0002219642 scopus 로고    scopus 로고
    • Learning Bayesian network structure from massive datasets: The "sparse candidate" algorithm
    • Stockholm, Sweden
    • N. Friedman, I. Nachman, and D. Peér. Learning Bayesian network structure from massive datasets: The "sparse candidate" algorithm. In Proc. 15th Conf. on Uncertainty in Artificial Intelligence, pp. 206-215, Stockholm, Sweden, 1999.
    • (1999) Proc. 15th Conf. on Uncertainty in Artificial Intelligence , pp. 206-215
    • Friedman, N.1    Nachman, I.2    Peér, D.3
  • 7
    • 0030193409 scopus 로고    scopus 로고
    • PALO: A probabilistic hill-climbing algorithm
    • R. Greiner. PALO: A probabilistic hill-climbing algorithm. Artificial Intelligence, 84:177-208, 1996.
    • (1996) Artificial Intelligence , vol.84 , pp. 177-208
    • Greiner, R.1
  • 8
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243, 1995.
    • (1995) Machine Learning , vol.20 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 9
  • 10
    • 0242529271 scopus 로고    scopus 로고
    • A general method for scaling up learning algorithms and its application to Bayesian networks
    • Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA
    • G. Hulten and P. Domingos. A general method for scaling up learning algorithms and its application to Bayesian networks. Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA, 2002.
    • (2002)
    • Hulten, G.1    Domingos, P.2
  • 13
    • 0001923944 scopus 로고
    • Hoeffding races: Accelerating model selection search for classification and function approximation
    • Morgan Kaufmann, San Mateo, CA
    • O. Maron and A. Moore. Hoeffding races: Accelerating model selection search for classification and function approximation. In Advances in Neural Information Processing Systems 6. Morgan Kaufmann, San Mateo, CA, 1994.
    • (1994) Advances in Neural Information Processing Systems 6
    • Maron, O.1    Moore, A.2
  • 16
    • 0242529272 scopus 로고    scopus 로고
    • Incremental maximization of non-instance-averaging utility functions with applications to knowledge discovery problems
    • Williamstown, MA
    • T. Scheffer and S. Wrobel. Incremental maximization of non-instance-averaging utility functions with applications to knowledge discovery problems. In Proc. 18th International Conf. on Machine Learning, pp. 481-488, Williamstown, MA, 2001.
    • (2001) Proc. 18th International Conf. on Machine Learning , pp. 481-488
    • Scheffer, T.1    Wrobel, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.