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Abstract: Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that scale up to large and possibly physically distributed databases. Meta-learning is a technique that seeks to compute higher-level classifiers... (Update)
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BibTeX entry: (Update)
Prodromidis, A.L., Chan, P.K.: Meta-learning in distributed data mining systems: Issues and Approaches. Book on Advances of Distributed Data Mining, editors Hillol Kargupta and Philip Chan, AAAI press, 2000. http://citeseer.ist.psu.edu/article/prodromidis00metalearning.html More
@misc{ prodromidis00metalearning,
author = "A. Prodromidis and P. Chan",
title = "Meta-learning in distributed data mining systems: Issues and Approaches",
text = "Prodromidis, A.L., Chan, P.K.: Meta-learning in distributed data mining
systems: Issues and Approaches. Book on Advances of Distributed Data Mining,
editors Hillol Kargupta and Philip Chan, AAAI press, 2000.",
year = "2000",
url = "citeseer.ist.psu.edu/article/prodromidis00metalearning.html" }
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