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University of Nevada, Las Vegas
On the effect of dynamic adjustment of recurrent network parameters on learning
Abstract
dc:description.abstractThe thesis examines sequential learning in a neural network model derived by M. I. Jordan and J. L. Elman. In each of three experiments, different network parameters are systematically altered in a series of simulations. Each simulation measures learning ability for a specific network configuration. Simulation results are consolidated to summarize each parameter's significance in the learning process.
Degree
thesis:*- Name thesis:degree_name
- Master of Science (MS)
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Computer Science
- Grantor dc:publisher
- University of Nevada, Las Vegas
- Year
- 1993
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Felgar, Stephen Lee
Rights
dc:rights- Statement dc:rights
-
- IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/
- Language dc:language
- English
Identifiers
dc:identifier.*- Identifier
- https://oasis.library.unlv.edu/rtds/303
- OAI identifier oai:identifier
- oai:oasis.library.unlv.edu:rtds-1302