Distributed algorithm to train neural networks using the Map Reduce paradigm
Cristian Mihai Barca and
Claudiu Dan Barca
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Cristian Mihai Barca: Electronics, Communications and Computers, University of Pitesti, Romania
Claudiu Dan Barca: The Romanian-American University, Bucharest, Romania
Database Systems Journal, 2017, vol. 8, issue 1, 3-11
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.
Keywords: Artificial Neural Networks; Machine Learning; Map-Reduce Hadoop; Distributed System (search for similar items in EconPapers)
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Persistent link: http://EconPapers.repec.org/RePEc:aes:dbjour:v:8:y:2017:i:1:p:3-11
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