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A case study of a distributed high-performance computing system for neurocomputing
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DOI：10.1016/S1383-7621(99)00017-X.png)
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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. (C) 2000 Elsevier Science B.V. All rights reserved.
Keyword:
neurocomputing
workstation cluster
statistical validation
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