Active Learning Based on New Localized Generalization Error Model for Training RBFNN

Abstract:

Article Preview

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.

Info:

Periodical:

Advanced Materials Research (Volumes 108-111)

Edited by:

Yanwen Wu

Pages:

1381-1385

DOI:

10.4028/www.scientific.net/AMR.108-111.1381

Citation:

S. F. Chen et al., "Active Learning Based on New Localized Generalization Error Model for Training RBFNN", Advanced Materials Research, Vols. 108-111, pp. 1381-1385, 2010

Online since:

May 2010

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Price:

$35.00

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