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Abstract: Machine learning is one of the successful application areas of fuzzy set theory and fuzzy logic, which provide soft, and thus tolerant, way of partitioning attribute domains. Theoretical results have shown that there is no (fuzzy) machine learning algorithm that is the best for all tasks. Therefore, for a particular task, it is very useful to have a tool to compare different algorithms in order to select appropriate ones, and to aid in the development of new algorithms, especially by combining... (Update)
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BibTeX entry: (Update)
@misc{ cao-fril,
author = "T.H. Cao and J.M. Rossiter",
title = "Fril++ for Machine Learning",
url = "citeseer.ist.psu.edu/621033.html" }
Citations (may not include all citations):976 Machine Learning (context) - Mitchell - 1997
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37 Data mining using MLC++: A machine learning library in C (context) - Kohavi, Sommerfield et al. - 1996
17 Fril - Fuzzy and Evidential Reasoning in Artificial Intellig.. (context) - Baldwin, Martin et al. - 1995
6 Fuzzy rule automation from data using mass assignment theory (context) - Baldwin - 1995
4 Inheritance and recognition in uncertain and fuzzy objectori.. (context) - Cao, Rossiter et al. - 2001
4 The UFO database model: dealing with imperfect information (context) - Van Gyseghem, De Caluwe - 1997
3 A hierarchical model of fuzzy classes (context) - Rossazza, Prade - 1997
3 Refining knowledge from uncertain relations - a fuzzy data b.. (context) - Baldwin, Martin - 1995
3 Towards conceptoriented databases (context) - Dubitzky, Bchner et al. - 1999
2 Uncertain inheritance and recognition as probabilistic defau.. (context) - Cao - 2001
1 A fuzzy object-oriented data model managing vague and uncert.. (context) - Bordogna, Pasi et al. - 1999
1 the implementation of Fril++ for object-oriented logic progr.. (context) - Cao, Rossiter et al. - 2001
1 The application of generalised fuzzy rules to machine learni.. (context) - Baldwin, Lawry et al. - 1998
1 McGraw-Hill (context) - Jeffrey - 1965
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