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||American English|
|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.|
|Number of pages||9|
|ISBN (Electronic)||1558603352, 9781558603356|
|State||Published - 1994|
|Event||11th International Conference on Machine Learning, ICML 1994 - New Brunswick, United States|
Duration: 10 Jul 1994 → 13 Jul 1994
|Name||Proceedings of the 11th International Conference on Machine Learning, ICML 1994|
|Conference||11th International Conference on Machine Learning, ICML 1994|
|Period||10/07/94 → 13/07/94|
Bibliographical notePublisher Copyright:
© 1994 Proceedings of the 11th International Conference on Machine Learning, ICML 1994. All rights reserved.