Learning Concept Descriptions from Examples with Errors
Proceedings of the Ninth International Joint Conference on Artificial Intelligence. Learning and Acquisition. Pages 634-635.
Abstract
This paper presents a scheme for learning complex descriptions, such as logic formulas, from examples with errors. The basis for learning is provided by a selection criterion which minimizes a combined measure of discrepancy of a description with training data, and complexity of a description. Learning rules for two types of descriptors are derived: one for finding descriptors with good average discrimination over a set of concepts, second for selecting the best descriptor for a specific concept. Once these descriptors are found, an unknown instance can be identified by a search using the descriptors of the first type for a fast screening of candidate concepts, and the second for the final selection of the closest concept.
@inproceedings{ijcai1985p122,
title = {Learning Concept Descriptions from Examples with Errors},
author = {Segen, Jakub},
booktitle = {Proceedings of the Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-85}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Aravind Joshi},
pages = {634--635},
year = {1985},
note = {Learning and Acquisition},
url = {https://ijcai.org/proceedings/1985/122},
}
TY - CPAPER TI - Learning Concept Descriptions from Examples with Errors AU - Segen, Jakub T2 - Proceedings of the Ninth International Joint Conference on Artificial Intelligence, IJCAI-85 PB - International Joint Conferences on Artificial Intelligence PY - 1985 SP - 634 EP - 635 UR - https://ijcai.org/proceedings/1985/122 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
