Learning vector quantization: The dynamics of winner-takes-all algorithms

Author:

Biehl Michael,Ghosh Anarta,Hammer Barbara

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference17 articles.

1. Bibliography on the self-organizing map (SOM) and learning vector quantization (LVQ), Neural networks research centre, Helsinki University of Technology, 2002.

2. M. Biehl, N. Caticha, The statistical physics of learning and generalization, in: M. Arbib (Ed.), Handbook of Brain Theory and Neural Networks, second ed., MIT Press, Cambridge, MA, 2003, pp. 1095–1098.

3. M. Biehl, A. Freking, A. Ghosh, G. Reents, A theoretical framework for analysing the dynamics of learning vector quantization. Technical Report 2004-9-02, Institute of Mathematics and Computing Science, University Groningen, available from 〈www.cs.rug.nl/biehl〉, 2004.

4. Margin analysis of the LVQ algorithm;Crammer,2002

5. Pattern Classification;Duda,2000

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