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Abstract: Many learning algorithms form concept descriptions composed of clauses, each of which covers some proportion of the positive training data and a small to zero proportion of the negative training data. This paper presents a method using likelihood ratios attached to clauses to classify test examples. One concept description is learned for each class. Each concept description competes to classify the test example using the likelihood ratios assigned to clauses of that concept... (Update)
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...using a simple criterion based on theory simplicity and training accuracy. This approach differs to that used in the HYDRA system [Ali Pazzani, 1993] in which the definitions for the different classes are learned and used in combination for classification. When FOIL is applied...
...domains development of noise tolerant ILP systems is one of essential topics. Some researches are worked in this area[DB92, BP91, AP93, Fur93, Dze95]. Easiness or tractability of a system is another aspect of practice. The size of a sample of a target logic program...
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BibTeX entry: (Update)
K. Ali and M. Pazzani, "HYDRA: A Noise-tolerant Relational Concept Learning Algorithm," Proc. 13th International Joint Conference on Artificial Intelligence, Chambery, France: Morgan Kaufmann, pp. 1064-1070, 1993. http://citeseer.ist.psu.edu/ali93hydra.html More
@inproceedings{ ali93hydra,
author = "K. M. Ali and M. J. Pazzani",
title = "{HYDRA}: {A} Noise-tolerant Relational Concept Learning Algorithm",
booktitle = "Proceedings of the 13th International Joint Conference on Artificial Intelligence",
publisher = "Morgan Kaufmann",
editor = "R. Bajcsy",
pages = "1064--1071",
year = "1993",
url = "citeseer.ist.psu.edu/ali93hydra.html" }
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