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Mathematical Remarks for Neural Networks

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Book cover Computational Intelligence

Part of the book series: Texts in Computer Science ((TCS))

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Abstract

The following sections treat mathematical topics that were presupposed in the text (Sect. 10.1 on straight line equations and Sect. 10.2 on regression), or side remarks, which would have disturbed the flow of the exposition (Sect. 10.3 on activation transformation in a Hopfield network).

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Notes

  1. 1.

    Note, however, that this can also be a disadvantage. If the data set contains “outliers” (that is, measurement values that due to random, disproportionally large measurement errors deviate strongly from the true value), the position of the regression line may be influenced heavily by very few measurement points (precisely the outliers), which can lead to an unusable result.

Reference

  • H. Heuser, Lehrbuch der Analysis, Teil 1+2 (Teubner, Stuttgart, Germany, 1988)

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Authors and Affiliations

  1. Otto-von-Guericke-University Magdeburg, Magdeburg, Sachsen-Anhalt, Germany

    Rudolf Kruse, Christian Borgelt, Christian Braune & Sanaz Mostaghim

  2. SAP Innovation Center, Potsdam, Germany

    Matthias Steinbrecher

Authors
  1. Rudolf Kruse
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  2. Christian Borgelt
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  3. Christian Braune
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  4. Sanaz Mostaghim
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  5. Matthias Steinbrecher
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Corresponding authors

Correspondence to Rudolf Kruse , Christian Borgelt , Christian Braune , Sanaz Mostaghim or Matthias Steinbrecher .

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© 2016 Springer-Verlag London

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Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M. (2016). Mathematical Remarks for Neural Networks. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-7296-3_10

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  • DOI: https://doi.org/10.1007/978-1-4471-7296-3_10

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-7294-9

  • Online ISBN: 978-1-4471-7296-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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