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Abstract: This paper concerns learning tasks that require the prediction of a continuous value rather than a discrete class. A general method is presented that allows predictions to use both instance-based and model-based learning. Results with three approaches to constructing models and with eight datasets demonstrate improvements due to the composite method. (Update)
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BibTeX entry: (Update)
Quinlan, R. (1993). Combining instance-based and model-based learning. In Machine Learning: Proceedings of the Tenth International Conference, Amherst, Massachusetts, pages 236--243. Morgan Kaufmann. http://citeseer.ist.psu.edu/quinlan93combining.html More
@inproceedings{ quinlan93combining,
author = "Quinlan, J. R.",
title = "Combining instance-based and model-based learning",
booktitle = "Proceedings of the Tenth International Conference on Machine Learning",
publisher = "Morgan Kaufmann",
address = "Amherst, Massachusetts",
pages = "236--243",
year = "1993",
url = "citeseer.ist.psu.edu/quinlan93combining.html" }
Citations (may not include all citations):2177 Programs for Machine Learning (context) - Quinlan
1535 Cambridge University Press (context) - Press, Flannery et al. - 1988
1262 Classification and regression trees (context) - Breiman, Friedman et al. - 1984
503 Instance-based learning algorithms (context) - Aha, Kibler et al. - 1991
269 Toward memorybased reasoning (context) - Stanfill, Waltz - 1986
101 Explorations in Parallel Distributed Processing (context) - McClelland, Rumelhart - 1988
92 Learning distributed representations of concepts (context) - Hinton - 1986
44 Learning with continuous classes - Quinlan - 1992
24 Instance-based prediction of real-valued attributes (context) - Kibler, Aha et al. - 1988
15 How neural networks learn from experience (context) - Hinton - 1992
6 Attributes of the performance of central processing units: a.. (context) - Ein-Dor, Feldmesser - 1987
2 A case study in machine learning - Quinlan
The graph only includes citing articles where the year of publication is known.
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