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Analogy-Based Preference Learning with Kernels

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KI 2019: Advances in Artificial Intelligence (KI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11793))

Abstract

Building on a specific formalization of analogical relationships of the form “A relates to B as C relates to D”, we establish a connection between two important subfields of artificial intelligence, namely analogical reasoning and kernel-based learning. More specifically, we show that so-called analogical proportions are closely connected to kernel functions on pairs of objects. Based on this result, we introduce the analogy kernel, which can be seen as a measure of how strongly four objects are in analogical relationship. As an application, we consider the problem of object ranking in the realm of preference learning, for which we develop a new method based on support vector machines trained with the analogy kernel. Our first experimental results for data sets from different domains (sports, education, tourism, etc.) are promising and suggest that our approach is competitive to state-of-the-art algorithms in terms of predictive accuracy.

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Notes

  1. 1.

    Extracted from FIFA official website: www.fifa.com.

  2. 2.

    Publicly available as an R package: http://cran.r-project.org/web/packages/svmpath.

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Author information

Authors and Affiliations

  1. Heinz Nixdorf Institute and Department of Computer Science, Intelligent Systems and Machine Learning Group, Paderborn University, Paderborn, Germany

    Mohsen Ahmadi Fahandar & Eyke Hüllermeier

Authors
  1. Mohsen Ahmadi Fahandar
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  2. Eyke Hüllermeier
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Corresponding author

Correspondence to Mohsen Ahmadi Fahandar .

Editor information

Editors and Affiliations

  1. Freie Universität Berlin, Berlin, Germany

    Christoph Benzmüller

  2. Universität Mannheim, Mannheim, Germany

    Heiner Stuckenschmidt

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Ahmadi Fahandar, M., Hüllermeier, E. (2019). Analogy-Based Preference Learning with Kernels. In: Benzmüller, C., Stuckenschmidt, H. (eds) KI 2019: Advances in Artificial Intelligence. KI 2019. Lecture Notes in Computer Science(), vol 11793. Springer, Cham. https://doi.org/10.1007/978-3-030-30179-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-30179-8_3

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  • Online ISBN: 978-3-030-30179-8

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