An Extensible Meta-Learning Approach for Scalable and Accurate Inductive Learning (1996)
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BibTeX
@MISC{Chan96anextensible,
author = {Philip Kin-Wah Chan},
title = {An Extensible Meta-Learning Approach for Scalable and Accurate Inductive Learning},
year = {1996}
}
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Abstract
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 for massive amounts of data, especially for applications in data mining. One approach to handling a large data set is to partition the data set into subsets, run the learning algorithm on each of the subsets, and combine the results. Moreover, data can be inherently distributed across multiple sites on the network and merging all the data in one location can be expensive or prohibitive. In this thesis we propose, investigate, and evaluate a meta-learning approach to integrating the results of mul...
