Title

Classification and Lumpability in the Stochastic Hopfield Model

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.

Department(s)

Mathematics and Statistics

Keywords and Phrases

Classification times; neural networks

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2001 Applied Probability Trust, All rights reserved.


Share

 
COinS