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Proceedings Paper

Research on the consistency of LVQ classifier
Author(s): Qing-Wen Zhou; Kai Wang; Qing-Ren Wang
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Paper Abstract

As a self-organizing artificial neural network model based on supervised learning, the LVQ classifier has been widely applied and deeply studied due to its good practical performance on the pattern recognition problems. The improved LVQ classifier have been greatly developed in previous works, and the experimental results on specific problems show that the improved LVQ classifier is indeed better than the standard learning algorithms proposed by Kohonen. Different from previous works, the consistency is studied in this paper to provide a theoretical support for the LVQ classifier. Furthermore, a simulation is included in this paper to provide an experimental support for our theoretical work.

Paper Details

Date Published: 20 August 2010
PDF: 6 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78202H (20 August 2010); doi: 10.1117/12.866975
Show Author Affiliations
Qing-Wen Zhou, Nankai Univ. (China)
Kai Wang, Nankai Univ. (China)
Qing-Ren Wang, Nankai Univ. (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du, Editor(s)

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