Learning to Classify Incomplete Examples
- Dale Schuurmans, AICML
- Russ Greiner, Dept of Computing Science; PI of AICML
Most research on supervised learning assumes the attributes of training and test examples are completely specified. Real-world data, however, is often incomplete. This paper studies the task of learning to classify incomplete test examples, given incomplete (resp., complete) training data. We first show that the performance task of classifying incomplete examples requires the use of default classification functions which demonstrate nonmonotonic classification behavior. We then extend the...
Citation
D. Schuurmans, R. Greiner. "Learning to Classify Incomplete Examples". Computational Learning Theory and Natural Learning Systems, MIT Press, 4, pp 87-105, May 1997.| Keywords: | |
| Category: | In Book |
BibTeX
@inbook{Schuurmans+Greiner:CLNL97,
author = {Dale Schuurmans and Russ Greiner},
title = {Learning to Classify Incomplete Examples},
Publisher = {MIT Press},
Volume = "4",
Chapter = "6",
Pages = {87-105},
year = 1997,
}Last Updated: May 29, 2017Submitted by Russ Greiner
