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Rough Deep Belief Network - Application to Incomplete Handwritten Digits Pattern Classification

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Information and Software Technologies (ICIST 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 538))

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Abstract

The rough deep belief networks (RDBN) are new modification of well known deep belief networks. Thanks to applied elements from Pawlak’s rough set theory, RDBNs are suitable in processing of incomplete patterns. In this paper we present the results of adaptation of this class of networks for classification of handwritten digits. The samples of the pattern applied in the learning and working processes are randomly corrupted. This allows to study the robustness of classifier for various levels of incompleteness.

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Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Wojciech K. Mleczko, Tomasz Kapuściński & Robert K. Nowicki

Authors
  1. Wojciech K. Mleczko
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  2. Tomasz Kapuściński
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  3. Robert K. Nowicki
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Corresponding author

Correspondence to Robert K. Nowicki .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Giedre Dregvaite

  2. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damasevicius

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Mleczko, W.K., Kapuściński, T., Nowicki, R.K. (2015). Rough Deep Belief Network - Application to Incomplete Handwritten Digits Pattern Classification. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2015. Communications in Computer and Information Science, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-24770-0_35

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  • DOI: https://doi.org/10.1007/978-3-319-24770-0_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24769-4

  • Online ISBN: 978-3-319-24770-0

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