Guiding Constructive Induction for Incremental Learning from Examples
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 is 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 LA IR 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 sub-tasks, and (4) constraining the search space by subtask-dependent constraints. I.
Cite
Text
Watanabe and Elio. "Guiding Constructive Induction for Incremental Learning from Examples." International Joint Conference on Artificial Intelligence, 1987.Markdown
[Watanabe and Elio. "Guiding Constructive Induction for Incremental Learning from Examples." International Joint Conference on Artificial Intelligence, 1987.](https://mlanthology.org/ijcai/1987/watanabe1987ijcai-guiding/)BibTeX
@inproceedings{watanabe1987ijcai-guiding,
title = {{Guiding Constructive Induction for Incremental Learning from Examples}},
author = {Watanabe, Larry and Elio, Renee},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1987},
pages = {293-296},
url = {https://mlanthology.org/ijcai/1987/watanabe1987ijcai-guiding/}
}