Guiding Constructive Induction for Incremental Learning from Examples
Proceedings of the Tenth International Joint Conference on Artificial Intelligence. Knowledge Acquisition. Pages 293-295.
Abstract
LAIR is a system that incrementally learns conjunctive concept descriptions from positive and negative examples. These concept descriptions are used to create and extend a domain theory that 1s applied, by means of constructive induction, to later learning tasks. Important issues for constructive induction are when to do it and how to control it. LAIR demonstrates how constructive induction can be controlled by (1) reducing it to simpler operations, (2) constraining the simpler operations to preserve relative correctness, (3) doing deductive inference on an as-needed basis to meet specific information requirements of learning subtasks, and (4) constraining the search space by subtaskdependentconstraints.
@inproceedings{ijcai1987p60,
title = {Guiding Constructive Induction for Incremental Learning from Examples},
author = {Watanabe, Larry and Elio, Renee},
booktitle = {Proceedings of the Tenth International Joint Conference on
Artificial Intelligence, {IJCAI-87}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John McDermott},
pages = {293--295},
year = {1987},
note = {Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1987/60},
}
TY - CPAPER TI - Guiding Constructive Induction for Incremental Learning from Examples AU - Watanabe, Larry AU - Elio, Renee T2 - Proceedings of the Tenth International Joint Conference on Artificial Intelligence, IJCAI-87 PB - International Joint Conferences on Artificial Intelligence PY - 1987 SP - 293 EP - 295 UR - https://ijcai.org/proceedings/1987/60 ER -Copied
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