Feed-forward neural networks for event classification in high-energy physics: where do we stand?

Alessandro De Angelis

doi:10.1117/12.235977

22 March 1996 Feed-forward neural networks for event classification in high-energy physics: where do we stand?

Alessandro De Angelis

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235977
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

Artificial Neural Networks (Feed-Forward Neural Networks in particular) have been proposed as a tool for event classification in High Energy Physics. After the first experimentation and an impressive success, they are nowadays a standard technique, for which an engineering is hoped.

Citation Download Citation

Alessandro De Angelis "Feed-forward neural networks for event classification in high-energy physics: where do we stand?", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235977

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