HYDRA: A Noise-tolerant Relational Concept Learning Algorithm
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 1064-1070.
Abstract
Many learning algorithms fonn 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 description. By testing on several artificial and "real world" domains, we demonstrate that attaching weights and allowing concept descriptions to compete to classify examples reduces an algorithm's susceptibility to noise.
@inproceedings{ijcai1993p147,
title = {HYDRA: A Noise-tolerant Relational Concept Learning Algorithm},
author = {Ali, Kamal M. and Pazzani, Michael J.},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {1064--1070},
year = {1993},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1993/147},
}
TY - CPAPER TI - HYDRA: A Noise-tolerant Relational Concept Learning Algorithm AU - Ali, Kamal M. AU - Pazzani, Michael J. T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 1064 EP - 1070 UR - https://ijcai.org/proceedings/1993/147 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
