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

✍ Scribed by D. Anguita; A. Boni; G. Parodi

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
215 KB
Volume
46
Category
Article
ISSN
1383-7621

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✦ Synopsis

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 eectively used for computational intensive neurocomputing tasks.

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