Learning to Assess from Pair-Wise Comparisons

  • J. Díez
  • J. J. del Coz
  • O. Luaces
  • F. Goyache
  • J. Alonso
  • A. M. Peña
  • A. Bahamonde
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2527)


In this paper we present an algorithm for learning a function able to assess objects. We assume that our teachers can provide a collection of pairwise comparisons but encounter certain difficulties in assigning a number to the qualities of the objects considered. This is a typical situation when dealing with food products, where it is very interesting to have repeatable, reliable mechanisms that are as objective as possible to evaluate quality in order to provide markets with products of a uniform quality. The same problem arises when we are trying to learn user preferences in an information retrieval system or in configuring a complex device. The algorithm is implemented using a growing variant of Kohonen’s Self-Organizing Maps (growing neural gas), and is tested with a variety of data sets to demonstrate the capabilities of our approach.


Neural Information Processing System Assessment Function Information Retrieval System Artificial Intelligence Research Regression Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • J. Díez
    • 1
  • J. J. del Coz
    • 2
  • O. Luaces
    • 2
  • F. Goyache
    • 1
  • J. Alonso
    • 2
  • A. M. Peña
    • 3
  • A. Bahamonde
    • 2
  1. 1.SERIDA-CENSYRA-SomióGijón (Asturias)Spain
  2. 2.Centro de Inteligencia ArtificialUniversidad de Oviedo at Gijón, Campus de ViesquesGijón (Asturias)Spain
  3. 3.Facultad de IngenieríaUniversidad Distrital Francisco José de CaldasBogotáColombia

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