Paper Title:
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.

  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, W. Liu, X. Y. Ren, H. J. Wang, "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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