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Abstract: In contrast to the standard machine learning tasks of classification and metric regression we investigate the problem of predicting variables of ordinal scale, a setting referred to as ordinal regression. The task of ordinal regression arises frequently in the social sciences and in information retrieval where human preferences play a major role. Also many multi--class problems are really problems of ordinal regression due to an ordering of the classes. Although the problem is rather novel to... (Update)
Context of citations to this paper: More
...can be controlled by making use of a quantity known as the margin. Let us consider the set H k of kernel classifiers [ Weston and Herbrich, 1999 ] 1 f(x) X i=1 ff i k(x i ; x) ff 2 R : 1) Here, k is referred to as a kernel and is assumed to be symmetric and...
.... that the Bayes optimal decision function on pairs of objects can result in a function p which is no longer transitive on X (Herbrich et al. 1999). Note also that the requirements of transitivity and asymmetry effectively reduce the space of admissible classification...
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BibTeX entry: (Update)
Ralf Herbrich, Thore Graepel, and Klaus Obermayer. Regression models for ordinal data: A machine learning approach. Technical report, TU Berlin, 1999. TR-99/03. http://citeseer.ist.psu.edu/herbrich99regression.html More
@misc{ herbrich99regression,
author = "R. Herbrich and T. Graepel and K. Obermayer",
title = "Regression models for ordinal data: A machine learning approach",
text = "Ralf Herbrich, Thore Graepel, and Klaus Obermayer. Regression models for
ordinal data: A machine learning approach. Technical report, TU Berlin,
1999. TR-99/03.",
year = "1999",
url = "citeseer.ist.psu.edu/herbrich99regression.html" }
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