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Abstract: Quantization of continuous variables is important in data analysis, especially for some model classes such as Bayesian networks and decision trees, which use discrete variables. Often, the discretization is based on the distribution of the input variables only whereas additional information, for example in form of class membership is frequently present and could be used to improve the quality of the results. In this paper, quantization methods based on equal width interval, maximum entropy,... (Update)
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BibTeX entry: (Update)
@inproceedings{ skubacz00quantization,
author = "Michal Skubacz and Jaakko Hollmen",
title = "Quantization of Continuous Input Variables for Binary Classification",
booktitle = "{IDEAL}",
pages = "42-47",
year = "2000",
url = "citeseer.ist.psu.edu/skubacz00quantization.html" }
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16 Synthesizing statistical knowledge from incomplete mixed-mod.. (context) - Wong, Chiu - 1987
12 Global discretization of continuous attributes as preprocess.. (context) - Chmielewski, Grzynala-Busse - 1996
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