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Reports (Research Report) Year : 2005

Analogical Dissimilarity: definition, algorithms and first experiments in machine learning

Laurent Miclet
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Arnaud Delhay

Abstract

This paper defines the notion of analogical dissimilarity between four objects, with a special focus on dissimilarity between objects structured as sequences. Firstly, it studies the case where the four objects have a null analogical dissimilarity, i.e. are in an analogical relation. Secondly, when one of these objects is unknown, it gives algorithms to compute it. In particular, it studies a new formulation of solving analogical equations on sequences, based on the edit distance between strings. Thirdly, it tackles the problem of defining analogical dissimilarity, which is a measure of how close four objects are from being in analogical relation. To finish, it gives learning algorithms, i.e. methods to find the triple of objects in a learning sample which has the least analogical dissimilarity with a given object.
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Dates and versions

inria-00070321 , version 1 (19-05-2006)

Identifiers

  • HAL Id : inria-00070321 , version 1

Cite

Laurent Miclet, Arnaud Delhay. Analogical Dissimilarity: definition, algorithms and first experiments in machine learning. [Research Report] RR-5694, INRIA. 2005, pp.60. ⟨inria-00070321⟩
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