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Distributed algorithm to train neural networks using the Map Reduce paradigm

Author

Listed:
  • Cristian Mihai BARCA

    (Electronics, Communications and Computers, University of Pitesti, Romania)

  • Claudiu Dan BARCA

    (The Romanian-American University, Bucharest, Romania)

Abstract

With rapid development of powerful computer systems during past decade, parallel and distributed processing becomes a significant resource for fast neural network training, even for real-time processing. Different parallel computing based methods have been proposed in recent years for the development of system performance. The two main methods are to distribute the patterns that are used for training - training set level parallelism, or to distribute the computation performed by the neural network - neural network level parallelism. In the present research work we have focused on the first method.

Suggested Citation

  • Cristian Mihai BARCA & Claudiu Dan BARCA, 2017. "Distributed algorithm to train neural networks using the Map Reduce paradigm," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 8(1), pages 3-11, July.
    • Handle: RePEc:aes:dbjour:v:8:y:2017:i:1:p:3-11
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    References listed on IDEAS

    as
    1. Ion LUNGU & Adela BÂRA & George CĂRUTASU & Alexandru PÎRJAN, & Simona-Vasilica OPREA, 2016. "Prediction Intelligent System In The Field Of Renewable Energies Through Neural Networks," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 85-102.
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