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Bimodal distribution removal

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New Trends in Neural Computation (IWANN 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 686))

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Abstract

A number of methods for cleaning up noisy training sets to improve generalisation have been proposed recently. Most of these methods perform well on artificially noisy data, but less well on real world data where it is difficult to distinguish between noisy data points from valid but rare data points.

We propose here a statistically based method which performs well on real world data and also provides a natural stopping criterion to terminate training.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, The University of New South Wales, Australia

    P. Slade & T. D. Gedeon

Authors
  1. P. Slade
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  2. T. D. Gedeon
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Editor information

José Mira Joan Cabestany Alberto Prieto

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© 1993 Springer-Verlag Berlin Heidelberg

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Slade, P., Gedeon, T.D. (1993). Bimodal distribution removal. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_155

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  • DOI: https://doi.org/10.1007/3-540-56798-4_155

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56798-1

  • Online ISBN: 978-3-540-47741-9

  • eBook Packages: Springer Book Archive

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