International Conference on Information and Software Technologies

Information and Software Technologies pp 400-411 | Cite as

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

  • Wojciech K. Mleczko
  • Tomasz Kapuściński
  • Robert K. Nowicki
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 538)

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.

Keywords

Deep belief network Rough set Missing features 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wojciech K. Mleczko
    • 1
  • Tomasz Kapuściński
    • 1
  • Robert K. Nowicki
    • 1
  1. 1.Institute of Computational IntelligenceCzestochowa University of TechnologyCzestochowaPoland

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