9410760 Barron The goal of the project is the development and proof of computationally feasible algorithms for training artificial neural networks that provide accurate approximation, estimation, and classification for general classes of functions in high dimensions. The analysis involves Markov chain convergence theory, based on a decomposition of the neural net likelihood function as a mixture of log-concave functions, and an information-theoretic analysis of Bayes estimators of neural nets. Practical computer implementation will also be investigated.

Agency
National Science Foundation (NSF)
Institute
Division of Electrical, Communications and Cyber Systems (ECCS)
Application #
9410760
Program Officer
Paul Werbos
Project Start
Project End
Budget Start
1994-09-15
Budget End
1997-08-31
Support Year
Fiscal Year
1994
Total Cost
$133,896
Indirect Cost
Name
Yale University
Department
Type
DUNS #
City
New Haven
State
CT
Country
United States
Zip Code
06520