Learning Preference Relations from Data

Authors:
Theodoros Evgeniou;Massimiliano Pontil
Affiliations:
-;-
Venue:
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Year:
2002

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Abstract

A number of learning tasks can be solved robustly using key concepts from statistical learning theory. In this paper we first summarize the main concepts of statistical learning theory, a framework in which certain learning from examples problems, namely classification, regression, and density estimation, have been studied in a principled way. We then show how the key concepts of the theory can be used not only for these standard learning from examples problems, but also for many others. In particular we discuss how to learn functions which model a preference relation. The goal is to illustrate the value of statistical learning theory beyond the standard framework it has been used until now.