Multilayered networks and the C-G uncertainty principle

Paolo Frasconi; Marco Gori

doi:10.1117/12.152638

19 August 1993 Multilayered networks and the C-G uncertainty principle

Paolo Frasconi, Marco Gori

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152638
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

Paolo Frasconi and Marco Gori "Multilayered networks and the C-G uncertainty principle", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152638

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available