Deferred Commitment in UNIMEM: Waiting to Learn
Abstract
Incremental learning allows a system to have available efficiently concepts learned so far from a continuous input stream. A problem for such systems is that they are often subject to order effects or simply need to wait to make decisions during processing. In this paper we describe a deferred commitment version of UNIMEM that often suspends processing of input until a time when a decision can be better made. Specifically, when the definition of a concept is in flax, the program will wait until it has been clarified. This method is designed as a bridge between incremental and non-incremental methods. We illustrate the system's performance on examples from the university and census domains.
Cite
Text
Lebowitz. "Deferred Commitment in UNIMEM: Waiting to Learn." International Conference on Machine Learning, 1988. doi:10.1016/B978-0-934613-64-4.50014-1Markdown
[Lebowitz. "Deferred Commitment in UNIMEM: Waiting to Learn." International Conference on Machine Learning, 1988.](https://mlanthology.org/icml/1988/lebowitz1988icml-deferred/) doi:10.1016/B978-0-934613-64-4.50014-1BibTeX
@inproceedings{lebowitz1988icml-deferred,
title = {{Deferred Commitment in UNIMEM: Waiting to Learn}},
author = {Lebowitz, Michael},
booktitle = {International Conference on Machine Learning},
year = {1988},
pages = {80-86},
doi = {10.1016/B978-0-934613-64-4.50014-1},
url = {https://mlanthology.org/icml/1988/lebowitz1988icml-deferred/}
}