Research on the consistency of LVQ classifier

Qing-Wen Zhou; Kai Wang; Qing-Ren Wang

doi:10.1117/12.866975

20 August 2010 Research on the consistency of LVQ classifier

Qing-Wen Zhou, Kai Wang, Qing-Ren Wang

Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78202H (2010) https://doi.org/10.1117/12.866975
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China

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.

Citation Download Citation

Qing-Wen Zhou, Kai Wang, and Qing-Ren Wang "Research on the consistency of LVQ classifier", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78202H (20 August 2010); https://doi.org/10.1117/12.866975

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available