Adaptive integration of multiple experts
Publication Type
Conference Proceeding Article
Publication Date
11-1995
Abstract
A novel method of integrating multiple experts in an adaptive manner is proposed. Each expert specializes in a particular sub-domain but performs poorly on the entire domain. By combining several such experts, the overall performance can be boosted significantly. To that effect, a supervised learning method, known as the supervised clustering and matching (SCM) algorithm, is used to combine the decisions of these experts based on their performance profile. By the fast and incremental learning capability of SCM, expert integration can be performed both on-line and off-line. Experiments on a sample benchmark problem illustrate that expert integration improves significantly upon the performance of each expert.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, 1995 November 27 - December 1
Volume
3
Identifier
10.1109/ICNN.1995.487327
City or Country
Perth, Western Australia
Citation
1