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EnglishWe 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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| Titolo: | A case study of a distributed high-performance computing system for neurocomputing | |
| Autori: | ||
| Data di pubblicazione: | 2000 | |
| Rivista: | ||
| Abstract: | 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. | |
| Handle: | http://hdl.handle.net/11567/315064 | |
| Appare nelle tipologie: | 01.01 - Articolo su rivista |
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