Adaptive integration of multiple experts

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 learnin...

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Main Authors: TEOW, Loo-Nin, TAN, Ah-hwee
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 1995
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Online Access:https://ink.library.smu.edu.sg/sis_research/6824
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.sis_research-78272022-01-27T03:48:03Z Adaptive integration of multiple experts TEOW, Loo-Nin TAN, Ah-hwee 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. 1995-11-27T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/6824 info:doi/10.1109/ICNN.1995.487327 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
TEOW, Loo-Nin
TAN, Ah-hwee
Adaptive integration of multiple experts
description 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.
format text
author TEOW, Loo-Nin
TAN, Ah-hwee
author_facet TEOW, Loo-Nin
TAN, Ah-hwee
author_sort TEOW, Loo-Nin
title Adaptive integration of multiple experts
title_short Adaptive integration of multiple experts
title_full Adaptive integration of multiple experts
title_fullStr Adaptive integration of multiple experts
title_full_unstemmed Adaptive integration of multiple experts
title_sort adaptive integration of multiple experts
publisher Institutional Knowledge at Singapore Management University
publishDate 1995
url https://ink.library.smu.edu.sg/sis_research/6824
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