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Neural Networks: A Statistician’s (Possible) View

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Classification and Knowledge Organization

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Summary

Within the past few years, neural networks (NNs) have emerged as a popular, rather general-purpose means of data processing and analysis. As in most applications they are employed to perform rather standard statistical tasks like regression analysis and classification, one might wonder what is really new about them. We shed some light on this issue from a statistician’s point of view by “translating” neural network terminology into more familiar terms and then discussing some of their most important properties. Particular attention is given to “supervised” classification, i.e., discriminant analysis.

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Authors and Affiliations

  1. Institut für Statistik und Wahrscheinlichkeitstheorie, Technische Universität Wien, A-1040, Wien, Austria

    K. Hornik

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  1. K. Hornik
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Editor information

Editors and Affiliations

  1. Institut für Medizinische Biometrie und Medizinische Informatik, University of Freiburg, Stefan-Meier-Straße 26, D-79104, Freiburg, Germany

    Rüdiger Klar

  2. University of Augsburg, D-86135, Augsburg, Germany

    Otto Opitz (Lehrstuhl für Mathematische Methoden der Wirtschaftswissenschaften) (Lehrstuhl für Mathematische Methoden der Wirtschaftswissenschaften)

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© 1997 Springer-Verlag Berlin Heidelberg

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Hornik, K. (1997). Neural Networks: A Statistician’s (Possible) View. In: Klar, R., Opitz, O. (eds) Classification and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59051-1_12

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  • DOI: https://doi.org/10.1007/978-3-642-59051-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62981-8

  • Online ISBN: 978-3-642-59051-1

  • eBook Packages: Springer Book Archive

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