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- W2096146901 abstract "The authors have published earlier a novel technique for the supervised training of feed-forward artificial neural networks using the Harmony Search algorithm. This paper proposes a parallel and distributed implementation method to speedup the execution time to address the training of larger pattern-classification benchmarking problems. The proposed method is a hybrid technique that adopts form the merits of two common parallel and distributed training methods, namely network partitioning and pattern partitioning. Experimentation is carried out on a large pattern-classification benchmarking problem using two Master-Slave parallel systems, a homogeneous system using a cluster computer and a heterogeneous system using a set of commodity computers connected via switched network. Results show that the proposed method attains a considerable speedup in comparison to the sequential implementation." @default.
- W2096146901 created "2016-06-24" @default.
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- W2096146901 date "2011-01-01" @default.
- W2096146901 modified "2023-10-16" @default.
- W2096146901 title "A Parallel & Distributed Implementation of the Harmony Search Based Supervised Training of Artificial Neural Networks" @default.
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- W2096146901 doi "https://doi.org/10.1109/isms.2011.49" @default.
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