We formalize a notion of loading information into connectionist networks that characterizes the training of feed-forward neural networks. This problem is NPcomplete, so we look for tractable subcases of the problem by placing constraints on the network architecture. The focus of these constraints is
โฆ LIBER โฆ
On the Sample Complexity for Nonoverlapping Neural Networks
โ Scribed by Michael Schmitt
- Book ID
- 110252215
- Publisher
- Springer
- Year
- 1999
- Tongue
- English
- Weight
- 60 KB
- Volume
- 37
- Category
- Article
- ISSN
- 0885-6125
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