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Implementing Contextual Neural Networks in Distributed Machine Learning Framework

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Intelligent Information and Database Systems (ACIIDS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10752))

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Abstract

Contextual neural networks are generalization of multilayer neural networks. They possess interesting property of automatic selection of data attributes needed for correct processing of given input vectors. To achieve that they are using neurons with conditional, multistep aggregation functions and error generalized error backpropagation algorithm based on self-consistency paradigm. According to the literature of the subject, currently there are no implementations of those models in high-performance machine learning platforms like Mahout or MLlib. In this paper we present initial results of implementation of contextual neural networks in distributed machine learning framework called H2O. The motivation behind this work is the need to analyze properties of contextual neural networks and conditional multi-step aggregation functions while solving large classification problems.

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Author information

Authors and Affiliations

  1. Wroclaw University of Science and Technology, Wroclaw, Poland

    Bartosz Jerzy Janusz & Krzysztof Wołk

Authors
  1. Bartosz Jerzy Janusz
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  2. Krzysztof Wołk
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Corresponding author

Correspondence to Bartosz Jerzy Janusz .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Quang Binh University, Dong Hoi City, Vietnam

    Duong Hung Hoang

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Rutgers University, Piscataway, New Jersey, USA

    Hoang Pham

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Janusz, B.J., Wołk, K. (2018). Implementing Contextual Neural Networks in Distributed Machine Learning Framework. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_20

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  • DOI: https://doi.org/10.1007/978-3-319-75420-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75419-2

  • Online ISBN: 978-3-319-75420-8

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