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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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 |
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Databases and Information Systems TEOW, Loo-Nin TAN, Ah-hwee Adaptive integration of multiple experts |
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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. |
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TEOW, Loo-Nin TAN, Ah-hwee |
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TEOW, Loo-Nin TAN, Ah-hwee |
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TEOW, Loo-Nin |
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Adaptive integration of multiple experts |
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Adaptive integration of multiple experts |
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Adaptive integration of multiple experts |
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Adaptive integration of multiple experts |
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Adaptive integration of multiple experts |
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adaptive integration of multiple experts |
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Institutional Knowledge at Singapore Management University |
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1995 |
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https://ink.library.smu.edu.sg/sis_research/6824 |
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