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Abstract: . In this paper, we describe a general approach to scaling data mining applications that we have come to call meta-learning. Meta-Learning refers to a general strategy that seeks to learn how to combine a number of separate learning processes in an intelligent fashion. We desire a meta-learning architecture that exhibits two key behaviors. First, the meta-learning strategy must produce an accurate final classification system. This means that a meta-learning architecture must produce a final... (Update)
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BibTeX entry: (Update)
Chan, P.K. & S.J. Stolfo (1996), On the Accuracy of Meta-learning for Scalable Data Mining, in Journal of Intelligent System, to appear. http://citeseer.ist.psu.edu/chan96accuracy.html More
@article{ chan97accuracy,
author = "Philip K. Chan and Salvatore J. Stolfo",
title = "On the Accuracy of Meta-Learning for Scalable Data Mining",
journal = "Journal of Intelligent Information Systems",
volume = "8",
number = "1",
pages = "5-28",
year = "1997",
url = "citeseer.ist.psu.edu/chan96accuracy.html" }
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1262 Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
537 A theory of the learnable (context) - Valiant - 1984 ACM DBLP
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273 The strength of weak learnability - Schapire - 1990
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47 Megainduction: A test flight (context) - Catlett - 1991 DBLP
45 Experiments on multistrategy learning by meta-learning - Chan, Stolfo - 1993 ACM
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6 Methods of combining multiple classifires and their applicat.. (context) - Xu, Krzyzak et al. - 1992
6 Scaling learning by meta-learning over disjoint and partiall.. - Chan, Stolfo - 1996
2 A study of explanation-based mehtods for inductive learning (context) - Flann, Dietterich - 1989
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