|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'503'724
Articles rated: 2609
23 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Non-Deterministic Learning Dynamics in Large Neural Networks due to Structural Data Bias | | H.C. Rae
; J.A.F. Heimel
; A.C.C. Coolen
; | | Date: |
13 Jul 2000 | | Journal: | J.Phys.A. 33 (2000) 8703-8722 | | Subject: | Disordered Systems and Neural Networks | cond-mat.dis-nn | | Abstract: | We study the dynamics of on-line learning in large perceptrons, for the case of training sets with a structural bias of the input vectors, by deriving exact and closed macroscopic dynamical laws using non-equilibrium statistical mechanical tools. In sharp contrast to the more conventional theories developed for homogeneously distributed or only weakly biased data, these laws are found to describe a non-trivial and persistently non-deterministic macroscopic evolution, and a generalisation error which retains both stochastic and sample-to-sample fluctuations, even for infinitely large networks. Furthermore, for the standard error-correcting microscopic algorithms (such as the perceptron learning rule) one obtains learning curves with distinct bias-induced phases. Our theoretical predictions find excellent confirmation in numerical simulations. | | Source: | arXiv, cond-mat/0007232 | | 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 Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER