A case study of a distributed high-performance computing system for neurocomputing

Description

We model here a distributed implementation of cross-stopping, a combination of cross-validation and early-stopping techniques, for the selection of the optimal architecture of feed-forward networks. Due to the very large computational demand of the method, we use the RAIN system (Redundant Array of Inexpensive workstations for Neurocomputing) as a target platform for the experiments and show that this kind of system can be effectively used for computational intensive neurocomputing tasks.

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