The Dynamic Universality of Sigmoidal Neural Networks
โ Scribed by Joe Kilian; Hava T. Siegelmann
- Book ID
- 112252245
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 428 KB
- Volume
- 128
- Category
- Article
- ISSN
- 0890-5401
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
We introduce a new method for proving explicit upper bounds on the VC dimension of general functional basis networks and prove as an application, for the first time, that the VC dimension of analog neural networks with the sigmoidal activation function \_( y)=1ร1+e & y is bounded by a quadratic poly
In the present paper we develop two algorithms, subset-based training (SBT) and subset-based training and pruning (SBTP), using the fact that the Jacobian matrices in sigmoid network training problems are usually rank deficient. The weight vectors are divided into two parts during training, accordin