Combination of an autoassociative morphological memory and the kernel method

Peter Sussner

doi:10.1117/12.403618

13 October 2000 Combination of an autoassociative morphological memory and the kernel method

Peter Sussner

Proceedings Volume 4120, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III; (2000) https://doi.org/10.1117/12.403618
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

We recently introduced a class of highly nonlinear associative memories called morphological associative memories (MAMs). Notable features of autoassociative morphological memories (AMMs) include optimal absolute storage capacity and one-step convergence. The fixed points can be characterized exactly in terms of the original patterns. Unfortunately, AMM fixed points include a large number of spurious memories. In this paper, we use a combination of a basic AMM model and the kernel method in order to eliminate most of the spurious memories while leaving other AMM properties intact. Furthermore, our new AMM model is more tolerant to noise than a basic AMM model and less dependent on kernel selection than the original kernel method.

Citation Download Citation

Peter Sussner "Combination of an autoassociative morphological memory and the kernel method", Proc. SPIE 4120, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III, (13 October 2000); https://doi.org/10.1117/12.403618

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