Abstract
In this paper we present a framework for performing incremental correction of approximate domain theories. Approximate domain theories are domain theories which are incomplete and/or incorrect. Based on initial information, belief values are assigned to different subsets of each clause in the domain theory. These belief values provide bias towards the correct refinement of the domain theory. We provide an incremental algorithm that refines the domain theory after observing positive and negative exemplars. Our algorithm requires a smaller number of misclassified exemplars than other algorithms presented in the literature.
Original language | English |
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Title of host publication | Proceedings of the 8th International Workshop on Machine Learning, ICML 1991 |
Editors | Lawrence A. Birnbaum, Gregg C. Collins |
Publisher | Morgan Kaufmann Publishers, Inc. |
Pages | 500-504 |
Number of pages | 5 |
ISBN (Electronic) | 1558602003, 9781558602007 |
DOIs | |
State | Published - 1991 |
Event | 8th International Workshop on Machine Learning, ICML 1991 - Evanston, United States Duration: 1 Jun 1991 → … |
Publication series
Name | Proceedings of the 8th International Workshop on Machine Learning, ICML 1991 |
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Conference
Conference | 8th International Workshop on Machine Learning, ICML 1991 |
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Country/Territory | United States |
City | Evanston |
Period | 1/06/91 → … |
Bibliographical note
Publisher Copyright:© ICML 1989.All rights reserved
Funding
Supported by the Office of Naval Research Grant N00014-90-J-1542
Funders | Funder number |
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Office of Naval Research | N00014-90-J-1542 |