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Separability is a Learner’s Best Friend

  • Conference paper
4th Neural Computation and Psychology Workshop, London, 9–11 April 1997

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

Geometric separability is a generalisation of linear separability, familiar to many from Minsky and Papert’s analysis of the Perceptron learning method. The concept forms a novel dimension along which to conceptualise learning methods. The present paper shows how geometric separability can be defined and demonstrates that it accurately predicts the performance of a at least one empirical learning method.

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References

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Author information

Authors and Affiliations

  1. Cognitive and Computing Sciences, University of Sussex, Brighton, BN1 9QH, UK

    Chris Thornton

Authors
  1. Chris Thornton
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Editor information

Editors and Affiliations

  1. Centre for Speech and Language, Department of Psychology, Birkbeck College, University of London, Malet Street, WC1E 7HX, London, UK

    John A. Bullinaria BSc, MSc, PhD

  2. Department of Psychology, University College London, Gower Street, WC1E 6BT, London, UK

    David W. Glasspool BSc, Msc  & George Houghton BA, MSc, PhD  & 

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© 1998 Springer-Verlag London Limited

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Thornton, C. (1998). Separability is a Learner’s Best Friend. In: Bullinaria, J.A., Glasspool, D.W., Houghton, G. (eds) 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1546-5_4

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  • DOI: https://doi.org/10.1007/978-1-4471-1546-5_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76208-9

  • Online ISBN: 978-1-4471-1546-5

  • eBook Packages: Springer Book Archive

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