mloss.org Choquistic Utilitaristic Regression http://mloss.orgUpdates and additions to Choquistic Utilitaristic Regression enFri, 17 Apr 2015 11:31:20 -0000Choquistic Utilitaristic Regression 1.00http://mloss.org/software/view/602/<html><p>This Matlab package implements a method for learning a choquistic regression model (represented by a corresponding Moebius transform of the underlying fuzzy measure), using the maximum likelihood approach proposed in [2], eqquiped by sigmoid normalization,
see [1]. For reasons of efficiency, it restricts to the 2-additive Choquet integral and implements the method, in which the Moebius transform is represented as a convex combination of a set of basis functions (for a detailed description of this method, see Section 4.2 in [3]).
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<p>[1] A. Fallah Tehrani, C. Labreuche, E. Huellermeier.
Choquistic Utilitaristic Regression, DA2PL-2014
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<p>[2] A. Fallah Tehrani, W. Cheng, K. Dembczynski, E. Huellermeier.
Learning Monotone Nonlinear Models using the Choquet Integral.
Machine Learning, 89(1):183-211, 2012.
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<p>[3] E. Huellermeier, A. Fallah Tehrani.
Efficient Learning of Classifiers based on the 2-additive
Choquet Integral.
In: C. Moewes and A. Nuernberger (eds.), Computational
Intelligence in Intelligent Data Analysis, Studies in
Computational Intelligence, Springer, pages 17-30, 2012.
</p></html>ali fallah tehraniFri, 17 Apr 2015 11:31:20 -0000http://mloss.org/software/rss/comments/602http://mloss.org/software/view/602/machine learningdata mining