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Fast Pattern Recognition and Deep Learning Using Multi-Rooted Binary Decision Diagrams

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8556))

Abstract

Binary decision diagrams (BDD) is a compact and efficient representation of Boolean functions with extensions available for sets and finite-valued functions. The key feature of the BDD is an ability to employ internal structure (not necessary known upfront) of an object being modelled in order to provide a compact in-memory representation. In this paper we propose application of the BDD for machine learning as a tool for fast general pattern recognition. Multiple BDDs are used to capture a sets of training samples (patterns) and to estimate the similarity of a given test sample with the memorized training sets. Then, having multiple similarity estimates further analysis is done using additional layer of BDDs or common machine learning techniques. We describe training algorithms for BDDs (supervised, unsupervised and combined), an approach for constructing multi-layered networks combining BDDs with traditional artificial neurons and present experimental results for handwritten digits recognition on the MNIST dataset.

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Author information

Authors and Affiliations

  1. Saint-Petersburg State University, Russia

    Dmitry Bugaychenko & Dmitry Zubarevich

Authors
  1. Dmitry Bugaychenko
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  2. Dmitry Zubarevich
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI,, Leipzig, Germany

    Petra Perner

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Bugaychenko, D., Zubarevich, D. (2014). Fast Pattern Recognition and Deep Learning Using Multi-Rooted Binary Decision Diagrams. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-08979-9_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08978-2

  • Online ISBN: 978-3-319-08979-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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