Adapting to Drift in Continuous Domains (1995)
| Venue: | In Proceedings of the 8th European Conference on Machine Learning |
| Citations: | 18 - 1 self |
BibTeX
@INPROCEEDINGS{Kubat95adaptingto,
author = {Miroslav Kubat and Gerhard Widmer},
title = {Adapting to Drift in Continuous Domains},
booktitle = {In Proceedings of the 8th European Conference on Machine Learning},
year = {1995},
pages = {307--310},
publisher = {Springer}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
The paper presents the system FRANN, which exploits the idea of radial-basis functions for the needs of learning in numeric domains under concept drift. The classification accuracy of the program compares favourably to that of older algorithms that are based on symbol manipulation. The system tolerates noise and is able to learn symbolic, numeric, and mixed concepts with nonlinear boundaries in environments with abrupt as well as gradual concept drift. Research area. Inductive learning Key words. concept drift, radial-basis functions Demo request. No Address for Correspondence: Miroslav Kubat, Institute for Systems Sciences, Johannes Kepler University, A-4040 Linz, Austria, e-mail: mirek@cast.uni-linz.ac.at 1 Introduction Recently, the problem of on-line learning in time-varying domains has received attention in the machine learning community. The essence is to make the learner recognize gradual or abrupt changes in the target concept and adjust accordingly the internal representa...
