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Italian Workshop on Neural Nets

WIRN 2002: Neural Nets pp 23–32Cite as

Learning Preference Relations from Data

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2486))

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.

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Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Via Roma 56, 53100, Siena, Italy

    Theodoros Evgniou

  2. Technology Management Department, INSEAD, Fontainebleau, France

    Massimiliano Pontil

Authors
  1. Theodoros Evgniou
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  2. Massimiliano Pontil
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Editor information

Editors and Affiliations

  1. Dipartimento di Fisica, “E.R. Caianiello”, Via S. Allende, Baronissi (Salerno), Italy

    Maria Marinaro

  2. International Institut for Advanced Scientific Studies “E.R. Caianiello”, IIASS, Via G. Pellegrino, 19, 84019, Vietri Sul Mare (Salerno), Italy

    Maria Marinaro

  3. Dipartimento di Matematica ed Informatica, Via S. Allende, Baronissi (Salerno), Italy

    Roberto Tagliaferri

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© 2002 Springer-Verlag Berlin Heidelberg

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Evgniou, T., Pontil, M. (2002). Learning Preference Relations from Data. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2002. Lecture Notes in Computer Science, vol 2486. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45808-5_2

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  • DOI: https://doi.org/10.1007/3-540-45808-5_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44265-3

  • Online ISBN: 978-3-540-45808-1

  • eBook Packages: Springer Book Archive

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