Off-line and on-line backpropagation methods with various levels of redundancy

Alessandra Di Medio; Stefano Fanelli; Marco Protasi

doi:10.1117/12.152630

19 August 1993 Off-line and on-line backpropagation methods with various levels of redundancy

Alessandra Di Medio, Stefano Fanelli, Marco Protasi

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152630
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

The performance of Back Propagation methods strongly depend on the following two choices: (1) use of off-line or on-line algorithm; (2) level of redundancy of the training set of data. Past investigations studied respectively off-line algorithms with a low degree of redundancy and on- line algorithms with a high degree of redundancy. In this paper we complete the framework considering on-line algorithms with a low level of information and off-line algorithms using training sets with 'redundancy of target data'.

Citation Download Citation

Alessandra Di Medio, Stefano Fanelli, and Marco Protasi "Off-line and on-line backpropagation methods with various levels of redundancy", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152630

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