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Active Learning Based on New Localized Generalization Error Model for Training RBFNN
Abstract:
A new active learning based on a new localized generalization error model is proposed in the paper for training RBFNN. The samples with largest local generalization error are selected and labelled. The experimental results show that the proposed algorithm is effective, which can select the most informative samples and fewer samples are necessary.
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1381-1385
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Online since:
May 2010
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© 2010 Trans Tech Publications Ltd. All Rights Reserved
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