DocumentCode
2344750
Title
Maximized mutual information using macrocanonical probability distributions
Author
Fry, Robert L.
Author_Institution
Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA
fYear
1994
fDate
27-29 Oct 1994
Firstpage
63
Abstract
A maximum entropy formulation leads to a neural network which is factorable in both form and function into individual neurons corresponding to the Hopfield neural model. A maximized mutual information criterion dictates the optimal learning methodology using locally available information
Keywords
Hopfield neural nets; learning (artificial intelligence); maximum entropy methods; probability; statistical analysis; Hopfield neural model; locally available information; macrocanonical probability distributions; maximized mutual information; maximum entropy formulation; neural network; optimal learning methodology; Biological system modeling; Biology computing; Degradation; Entropy; Equations; Mutual information; Neural networks; Neurons; Physics; Sampling methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location
Alexandria, VA
Print_ISBN
0-7803-2761-6
Type
conf
DOI
10.1109/WITS.1994.513892
Filename
513892
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