Classification and Lumpability in the Stochastic Hopfield Model

Author

Robert Paige L., Missouri University of Science and TechnologyFollow

Abstract

Connections between classification and lumpability in the stochastic Hopfield model (SHM) are explored and developed. A simplification of the SHM's complexity based upon its inherent lumpability is derived. Contributions resulting from this reduction in complexity include: (i) computationally feasible classification time computations (ii) a development of techniques for enumerating the stationary distribution of the SHM's energy function and (iii) a characterization of the set of possible absorbing states of the Markov chain associated with the zero temperature SHM.

Recommended Citation

R. Paige, "Classification and Lumpability in the Stochastic Hopfield Model," Advances in Applied Probability, Applied Probability Trust, Jan 2001.

The definitive version is available at https://doi.org/10.1239/aap/1011994037

Department(s)

Mathematics and Statistics

Keywords and Phrases

Classification times; neural networks

International Standard Serial Number (ISSN)

0001-8678

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2001 Applied Probability Trust, All rights reserved.

Publication Date

01 Jan 2001