Share Email Print

Proceedings Paper

Multilayered networks and the C-G uncertainty principle
Author(s): Paolo Frasconi; Marco Gori
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The experience gained in many experiments with neural networks has shown that many challenging problems are still hard to solve, since the learning process becomes very slow, often leading to sub-optimal solutions. In this paper we analyze this problem for the case of two-layered networks by discussing on the joint behavior of the algorithm convergence and the generalization to new data. We suggest two scores for generalization and optimal convergence that behave like conjugate variable in Quantum Mechanics. As a result, the requirement of increasing the generalization is likely to affect the optimal convergence. This suggests that 'difficult' problems are better face with biased-models, somewhat tuned on the task to be solved.

Paper Details

Date Published: 19 August 1993
PDF: 6 pages
Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); doi: 10.1117/12.152638
Show Author Affiliations
Paolo Frasconi, Univ. di Firenze (Italy)
Marco Gori, Univ. di Firenze (Italy)

Published in SPIE Proceedings Vol. 1966:
Science of Artificial Neural Networks II
Dennis W. Ruck, Editor(s)

© SPIE. Terms of Use
Back to Top