Abstract
We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar networks.
Original language | English |
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Journal | I E E E Transactions on Pattern Analysis and Machine Intelligence |
Volume | 12 |
Pages (from-to) | 993-1001 |
ISSN | 0162-8828 |
Publication status | Published - 1990 |
Keywords
- fault tolerant computing
- neural networks
- crossvalidation