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

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