Abstract
This paper introduces a new classification technique called degree-of-provedness classification, or DOP-classification. This technique exploits information implicit in the structure of a possibly incomplete or incorrect domain theory in order to improve classification accuracy. It is also shown how DOP-classification can be used to identify theories for which theory revision is unnecessary (because the unrevised theory can be used directly by DOP-classification to achieve near-perfect classification accuracy) or insufficient (because the initial theory is so flawed that it would be preferable to induce a new theory directly from examples).
Original language | English |
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Title of host publication | Proceedings of the 11th International Conference on Machine Learning, ICML 1994 |
Editors | William W. Cohen, Haym Hirsh |
Publisher | Morgan Kaufmann Publishers, Inc. |
Pages | 139-147 |
Number of pages | 9 |
ISBN (Electronic) | 1558603352, 9781558603356 |
DOIs | |
State | Published - 1994 |
Event | 11th International Conference on Machine Learning, ICML 1994 - New Brunswick, United States Duration: 10 Jul 1994 → 13 Jul 1994 |
Publication series
Name | Proceedings of the 11th International Conference on Machine Learning, ICML 1994 |
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Conference
Conference | 11th International Conference on Machine Learning, ICML 1994 |
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Country/Territory | United States |
City | New Brunswick |
Period | 10/07/94 → 13/07/94 |
Bibliographical note
Publisher Copyright:© 1994 Proceedings of the 11th International Conference on Machine Learning, ICML 1994. All rights reserved.