Maintaining the utility of learned knowledge using model-based adaptive control

The overfit problem in empirical learning and the utility problem in analytical learning both describe a common behavior of machine learning methods: the eventual degradation of performance due to increasing amounts of learned knowledge. Plotting the performance of the changing knowledge during execution of a machine learning method (the performance response) reveals similar curves for several methods. The performance response generally indicates a single peak performance greater than that attained by popular pruning techniques. The similarity in performance responses suggests a parameterized model relating performance to the amount of learned knowledge. Given this model, a model-based adaptive control (MBAC) approach can be used to update the model based on feedback from the performance element and make control decisions regarding the amount of knowledge to be learned or unlearned.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Holder, Lawrence B.

Contributors dc:contributor

• Rendell, Larry A.

• Artificial Intelligence
• Computer Science

Statement dc:rights

• Copyright 1991 Holder, Lawrence Bruce, Jr

Language dc:language

eng

Identifier

AAI9210839
(UMI)AAI9210839

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/22737