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International Conference on Artificial Neural Networks

ICANN 2012: Artificial Neural Networks and Machine Learning – ICANN 2012 pp E1Cite as

Erratum: Robust Training of Feedforward Neural Networks Using Combined Online/Batch Quasi-Newton Techniques

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7553))

Abstract

The paper starting on page 74 of this volume has been retracted as it contains a large amount of text taken directly from two previous publications by the same author.

The original online version for this chapter can be found at http://dx.doi.org/10.1007/978-3-642-33266-1_10

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Author information

Authors and Affiliations

  1. Department of Information Science, Shonan Institute of Technology, 1-1-25, Tsujido-Nishikaigan, Fujisawa, Kanagawa, 251-8511, Japan

    Hiroshi Ninomiya

Authors
  1. Hiroshi Ninomiya
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Editor information

Editors and Affiliations

  1. Neuro Heuristic Research Group, University of Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

  2. Department of Informatics, Nicolaus Copernicus University, 87-100, Toruń, Poland

    Włodzisław Duch

  3. Center for Complex Systems Studies, Kalamazoo College, 49006, Kalamazoo, MI, USA

    Péter Érdi

  4. Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, 16146, Genoa, Italy

    Francesco Masulli

  5. Institut für Neuroinformatik, Universität Ulm, 89069, Ulm, Germany

    Günther Palm

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Ninomiya, H. (2012). Erratum: Robust Training of Feedforward Neural Networks Using Combined Online/Batch Quasi-Newton Techniques. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_72

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  • DOI: https://doi.org/10.1007/978-3-642-33266-1_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33265-4

  • Online ISBN: 978-3-642-33266-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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