A Comparitive Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems
Abstract
The utility problem in learning systems occurs when knowledge learned in an attempt to improve a system's performance degrades performance instead. We present a methodology for the analysis of utility problems which uses computational models of problem solving systems to isolate the root causes of a utility problem, to detect the threshold conditions under which the problem will arise, and to design strategies to eliminate it. We present models of case-based reasoning and control-rule learning systems and compare their performance with respect to the swamping utility problem. Our analysis suggests that case-based reasoning systems are more resistant to the utility problem than control-rule learning systems.
Cite
Text
Jr. and Ram. "A Comparitive Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems." European Conference on Machine Learning, 1995. doi:10.1007/3-540-59286-5_54Markdown
[Jr. and Ram. "A Comparitive Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems." European Conference on Machine Learning, 1995.](https://mlanthology.org/ecmlpkdd/1995/jr1995ecml-comparitive/) doi:10.1007/3-540-59286-5_54BibTeX
@inproceedings{jr1995ecml-comparitive,
title = {{A Comparitive Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems}},
author = {Jr., Anthony G. Francis and Ram, Ashwin},
booktitle = {European Conference on Machine Learning},
year = {1995},
pages = {138-150},
doi = {10.1007/3-540-59286-5_54},
url = {https://mlanthology.org/ecmlpkdd/1995/jr1995ecml-comparitive/}
}