Storage Capacity of the Tilinglike Learning Algorithm
Disordered Systems and Neural Networks
2009-10-31 v1 Statistical Mechanics
Abstract
The storage capacity of an incremental learning algorithm for the parity machine, the Tilinglike Learning Algorithm, is analytically determined in the limit of a large number of hidden perceptrons. Different learning rules for the simple perceptron are investigated. The usual Gardner-Derrida one leads to a storage capacity close to the upper bound, which is independent of the learning algorithm considered.
Cite
@article{arxiv.cond-mat/0008162,
title = {Storage Capacity of the Tilinglike Learning Algorithm},
author = {Arnaud Buhot and Mirta B. Gordon},
journal= {arXiv preprint arXiv:cond-mat/0008162},
year = {2009}
}
Comments
Proceedings of the Conference Disordered and Complex Systems, King's College, London, July 2000. 6 pages, 1 figure, uses aipproc.sty