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Abstract: An Extensible Meta-Learning Approach for Scalable and Accurate Inductive Learning Philip Kin-Wah Chan Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of ubiquitous network computing, it is likely that orders of magnitude more data in databases will be available for various learning problems of real world importance. Some learning algorithms assume that the entire data set fits into main memory, which is not feasible... (Update)
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BibTeX entry: (Update)
P. Chan. An Extensible Meta-Learning Approach for Scalable and Accurate Inductive Learning. PhD thesis, Department of Computer Science, Columbia University, New York, NY, 1996. http://citeseer.ist.psu.edu/chan96extensible.html More
@misc{ chan96extensible,
author = "P. Chan",
title = "An Extensible Meta-Learning Approach for Scalable and Accurate Inductive
Learning",
text = "P. Chan. An Extensible Meta-Learning Approach for Scalable and Accurate
Inductive Learning. PhD thesis, Department of Computer Science, Columbia
University, New York, NY, 1996.",
year = "1996",
url = "citeseer.ist.psu.edu/chan96extensible.html" }
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The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.cs.columbia.edu/~pkc/): More
Experiments on Multistrategy Learning by Meta-Learning - Chan (1993) (Correct)
Toward Parallel and Distributed Learning by Meta-Learning - Chan (1993) (Correct)
Toward Scalable and Parallel Inductive Learning: A Case Study in.. - Chan (1994) (Correct)
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