Jeffreys' prior for layered neural networks

Yoichi Motomura

doi:10.1117/12.205194

6 April 1995 Jeffreys' prior for layered neural networks

Yoichi Motomura

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205194
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

In this paper, Jeffreys' prior for neural networks is discussed in the framework of the Bayesian statistics. For a good performance of generalization, the regularization methods which reduce both cost function and regularization term are commonly used. In the Bayesian statistics, the regularization term can be naturally derived from prior distribution of parameters. Jeffreys' prior is known as a typical non-informative objective prior. In the case of neural networks, however, it is not easy to express Jeffreys' prior as a simple function of parameters. In this paper, some numerical analysis of Jeffreys' prior for neural networks is given. The approximation of Jeffreys' prior is given from a parameter transformation getting to make Jeffreys' prior as a simple function. Some learning techniques are also discussed as applications of these results.

Citation Download Citation

Yoichi Motomura "Jeffreys' prior for layered neural networks", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205194

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