Learning from infinite data in finite time

Advances in Neural Information Processing Systems

Volumn , Issue , 2002, Pages

  Domingos, Pedro ^a   Hulteri, Geoff ^a  

^a University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EM ALGORITHMS; FINITE TIME; GENERAL METHOD; LARGE DATASETS; MIXTURES OF GAUSSIANS; TIME COMPLEXITY; UPPER BOUND;

ALGORITHMS;

EID: 84899023111     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39:1-38, 1977.
2. P. Domingos and G. Hulten. Mining high-speed data streams. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71-80, Boston, MA, 2000. ACM Press.
3. P. Domingos and G. Hulten. A general method for scaling up machine learning algorithms and its application to clustering. In Proceedings of the Eighteenth In-ternational Conference on Machine Learning, pp. 106-113, Williamstown, MA, 2001. Morgan Kaufmann.
4. W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58:13-30, 1963.
5. C. Meek, B. Thiesson, and D. Heckerman. The learning-curve method applied to clustering. Technical Report MSR-TR-01-34, Microsoft Research, Redmond, WA, 2000.
6. B. Thiesson, C. Meek, and D. Heckerman. Accelerating EM for large databases. Technical Report MSR-TR-99-31, Microsoft Research, Redmond, WA, 2001.
7. A. Wolman, G. Voelker, N. Sharma, N. Cardwell, M. Brown, T. Landray, D. Pinnel, A. Karlin, and H. Levy. Organization-based analysis of Web-object sharing and caching. In Proceedings of the Second USENIX Conference on Internet Technologies and Systems, pp. 25-36, Boulder, CO, 1999.
8. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient data clustering method for very large databases. In Proceedings of the 1996 ACM SIC- MOD International Conference on Management of Data, pp. 103-114, Montreal, Canada, 1996. ACM Press.