Version Spaces: A Candidate Elimination Approach to Rule Learning
Proceedings of the Fifth International Joint Conference on Artificial Intelligence. Knowledge Acquisition. Pages 305-309.
Abstract
An important research problem in artificial intelligence is the study of methods for learning general concepts or rules from a set of training instances. An approach to this problem is presented which is guaranteed to find, without backtracking, all rule versions consistent with a set of positive and negative training instances. The algorithm put forth uses a representation of the space of those rules consistent with the observed training data. This "rule version space" is modified in response to new training instances by eliminating candidate rule versions found to conflict with each new instance. The use of version spaces is discussed in the context of Meta-DENDRAL, a program which learns rules in the domain of chemical spectroscopy.
@inproceedings{ijcai1977p68,
title = {Version Spaces: A Candidate Elimination Approach to Rule Learning},
author = {Mitchell, Tom M.},
booktitle = {Proceedings of the Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-77}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Raj Reddy},
pages = {305--309},
year = {1977},
note = {Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1977/68},
}
TY - CPAPER TI - Version Spaces: A Candidate Elimination Approach to Rule Learning AU - Mitchell, Tom M. T2 - Proceedings of the Fifth International Joint Conference on Artificial Intelligence, IJCAI-77 PB - International Joint Conferences on Artificial Intelligence PY - 1977 SP - 305 EP - 309 UR - https://ijcai.org/proceedings/1977/68 ER -Copied
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