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Fast Sensitivity-Based Training of BP-Networks

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Book cover Artificial Neural Networks and Machine Learning – ICANN 2014 (ICANN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8681))

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Abstract

Sensitivity analysis became an acknowledged tool used to study the performance of artificial neural networks. Sensitivity analysis allows to assess the influence, e.g., of each neuron or weight on the final network output. In particular various feature selection and pruning strategies are based on this capability. In this paper, we will present a new approximative sensitivity-based training algorithm yielding robust neural networks with generalization capabilities comparable to its exact analytical counterpart, yet much faster.

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References

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University in Prague, Malostranské nám. 25, 118 00, Prague, Czech Republic

    Iveta Mrázová & Zuzana Petříčková

Authors
  1. Iveta Mrázová
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  2. Zuzana Petříčková
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Editor information

Editors and Affiliations

  1. Department of Informatics, University of Hamburg, Vogt-Kölln-Straße 30, 22527, Hamburg, Germany

    Stefan Wermter , Cornelius Weber  & Sven Magg ,  & 

  2. Department of Informatics, Nicolaus Compernicus University, ul. Grudziądzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Modern Languages, University of Helsinki, P.O. Box 24, 00014, Helsinki, Finland

    Timo Honkela

  4. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl. 25A, 1113, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  5. Institute of Neural Information Processing, University of Ulm, 89069, Oberer Eselsberg, Ulm, Germany

    Günther Palm

  6. Department of Information Systems, Quartier UNIL-Dorigny, Bâtiment Internef, University of Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

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© 2014 Springer International Publishing Switzerland

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Mrázová, I., Petříčková, Z. (2014). Fast Sensitivity-Based Training of BP-Networks. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_64

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  • DOI: https://doi.org/10.1007/978-3-319-11179-7_64

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11178-0

  • Online ISBN: 978-3-319-11179-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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