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Abstract: In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm is that its running time becomes independent of the size of the database, while the decisions made are essentially identical to those that would be made given infinite data. The method works within pre-specified memory limits and, as long as the data is iid, only requires accessing it sequentially. It gives anytime... (Update)
Context of citations to this paper: More
.... This is based on our previous work in applying subsampling techniques to propositional learners [Domingos and Hulten, 2000; Hulten and Domingos, 2002] Beyond this, we envisage that intelligent control of which tuples a learner looks at, and which join paths it pursues,...
...continuously generate data, and the distribution of this data often changes drastically as time goes by. In previous work [Hulten and Domingos, 2002] we developed a framework capable of semi automatically scaling up a wide class of propositional learning algorithms to address...
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BibTeX entry: (Update)
G. Hulten and P. Domingos. Mining complex models from arbitrarily large databases in constant time. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 525--531, Edmonton, Alberta, Canada, 2002. ACM Press. http://citeseer.ist.psu.edu/hulten02mining.html More
@misc{ hulten02mining,
author = "G. Hulten and P. Domingos",
title = "Mining complex models from arbitrarily large databases in constant time",
text = "G. Hulten and P. Domingos. Mining complex models from arbitrarily large
databases in constant time. In Proceedings of the Eighth ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, pages 525--531, Edmonton,
Alberta, Canada, 2002. ACM Press.",
year = "2002",
url = "citeseer.ist.psu.edu/hulten02mining.html" }
Citations (may not include all citations):351 Learning Bayesian networks: The combination of knowledge and.. - Heckerman, Geiger et al. - 1995
157 Probability inequalities for sums of bounded random variable.. (context) - Hoe - 1963
92 Mining high-speed data streams - Domingos, Hulten - 2000
91 Improving generalization with active learning - Cohn, Atlas et al. - 1994
81 Sequential analysis (context) - Wald - 1947
53 Mining time-changing data streams - Hulten, Spencer et al. - 2001
27 the sample complexity of learning Bayesian networks - Friedman, Yakhini - 1996
23 PALO: A probabilistic hill-climbing algorithm - Greiner - 1996
21 Adaptive sampling methods for scaling up knowledge discovery.. - Domingo, Gavalda et al. - 2002
17 sparse candidate (context) - Friedman, Nachman et al. - 1999
12 ding races: Accelerating model selection search for classifi.. (context) - Maron, Moore - 1994
11 KDD-Cup (context) - Kohavi, Brodley et al. - 2000
8 cient progressive sampling (context) - Provost, Jensen et al. - 1999
7 Learning from infinite data in finite time - Domingos, Hulten - 2002
3 Incremental maximization of non-instance-averaging utility f.. (context) - Sche, Wrobel - 2001
2 The learning-curve method applied to model-based clustering (context) - Meek, Thiesson et al. - 2002
1 A general method for scaling up learning algorithms and its .. (context) - Hulten, Domingos - 2002
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