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Teaching Machines to Learn by Metaphors

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Omer Levy and Shaul Markovitch. Teaching Machines to Learn by Metaphors. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 991-997 Toronto, Canada, 2012.

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Abstract

Humans have an uncanny ability to learn new concepts with very few examples. Cognitive theories have suggested that this is done by utilizing prior experience of related tasks. We propose to emulate this process in machines, by transforming new problems into old ones. These transformations are called metaphors. Obviously, the learner is not given a metaphor, but must acquire one through a learning process. We show that learning metaphors yield better results than existing transfer learning methods. Moreover, we argue that metaphors give a qualitative assessment of task relatedness.

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Keywords: Machine Learning, Classification, Transfer Learning, Induction

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Secondary Keywords:

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Online version:

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Bibtex entry:

 @inproceedings{Levy:2012:TML,
  Author = {Omer Levy and Shaul Markovitch},
  Title = {Teaching Machines to Learn by Metaphors},
  Year = {2012},
  Booktitle = {Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence},
  Pages = {991--997},
  Address = {Toronto, Canada},
  Url = {http://www.cs.technion.ac.il/~shaulm/papers/pdf/Levy-Markovitch-AAAI2012.pdf},
  Keywords = {Machine Learning, Classification, Transfer Learning, Induction},
  Abstract = {
    Humans have an uncanny ability to learn new concepts with very few
    examples. Cognitive theories have suggested that this is done by
    utilizing prior experience of related tasks. We propose to emulate
    this process in machines, by transforming new problems into old
    ones. These transformations are called metaphors. Obviously, the
    learner is not given a metaphor, but must acquire one through a
    learning process. We show that learning metaphors yield better
    results than existing transfer learning methods. Moreover, we
    argue that metaphors give a qualitative assessment of task
    relatedness.
  }

  }