Abstract
In this paper a competitive neural network with binary synaptic weights is proposed. The aim of this network is to cluster or categorize binary input data. The neural network uses a learning mechanism based on activity levels that generates new binary synaptic weights that evolve toward medianoids of the clusters or categorizes that are being formed by the process units of the network, since the medianoid is the better representation of a cluster for binary data when the Hamming distance is used. The proposed model has been applied to codebook generation in vector quantization (VQ) for binary fingerprint image compression. The binary neural network find a set of representative vectors (codebook) for a given training set minimizing the average distortion.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Grossberg, S. Adaptative pattern classification and universal recording: I. Parallel develop ment and coding of neural detectors, Biolog. Cybernetics, 23 (1976) 121–134.
von der Malsburg, C. Self-organization of orientation sensitive cells in the striate cortex, Kybernetik, 14 (1973) 85–100.
Amari, S. and Takeuchi, M. Mathematical theory on formation of category detecting nerve cells, Biological Cybernetics, 29 (1978) 127–136.
Amari, S-I. Field theory of self-organizing neural nets, IEEE Transactions on Systems, Man and Cybernetics, SMC-13 (1983) 741–748.
Bienstock, E., Cooper, E. & Munro, P. Theory for the development of neural selectivity: Orientation specificy and binocular interaction in visual cortex, Journal of Neuroscience, 2 (1982) 32–48.
Rumelhart, D. and Zipser, D. (1985). Feature discovery by competitive learning, Cognitive Science, 9(1985)75–112.
Carpenter, G. A. and Grossberg, S. A massively paralell architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing 37(1987)54–115.
Hassoun M.H. Fundamentals of Artificial Neural Networks. The MIT Press, Cambridge, 1995.
Duda R.O., Hart P.E. and Stork D.G. Pattern Classification. John Wiley & Son, New York, 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muñoz-Pérez, J., García-Bernal, M.A., de Guevara-Lòpez, I.L., Gomez-Ruiz, J. (2003). BICONN: A Binary Competitive Neural Network. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_55
Download citation
DOI: https://doi.org/10.1007/3-540-44868-3_55
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40210-7
Online ISBN: 978-3-540-44868-6
eBook Packages: Springer Book Archive