|
| | | | |
| | | |
| Stat |
Members: 3644
Articles: 2'497'992
Articles rated: 2609
16 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Dynamics of Learning with Restricted Training Sets II: Tests and Applications | | A.C.C. Coolen
; D. Saad
; | | Date: |
29 Sep 1999 | | Subject: | Disordered Systems and Neural Networks | cond-mat.dis-nn | | Abstract: | We apply a general theory describing the dynamics of supervised learning in layered neural networks in the regime where the size p of the training set is proportional to the number of inputs N, as developed in a previous paper, to several choices of learning rules. In the case of (on-line and batch) Hebbian learning, where a direct exact solution is possible, we show that our theory provides exact results at any time in many different verifiable cases. For non-Hebbian learning rules, such as Perceptron and AdaTron, we find very good agreement between the predictions of our theory and numerical simulations. Finally, we derive three approximation schemes aimed at eliminating the need to solve a functional saddle-point equation at each time step, and assess their performance. The simplest of these schemes leads to a fully explicit and relatively simple non-linear diffusion equation for the joint field distribution, which already describes the learning dynamics surprisingly well over a wide range of parameters. | | Source: | arXiv, cond-mat/9909428 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser claudebot
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER