Abstract
A method of estimation of the quality of data identification by a parametric perceptron is presented. The method allows one to combine the parametric perceptrons into a committee. It is shown by the example of the Potts perceptrons that the storage capacity of the committee grows linearly with the increase of the number of perceptrons forming the committee. The combination of perceptrons into a committee is useful when given task parameters (image dimension and chromaticity, the number of patterns, distortion level, identification reliability) one perceptron is unable to solve the identification task. The method can be applied in q-ary or binary pattern identification task.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kanter, I.: Potts-glass models of neural networks. Physical Review A 37(7), 2739–2742 (1988)
Cook, J.: The mean-field theory of a Q-state neural network model. Journal of Physics A 22, 2000–2012 (1989)
Bolle, D., Dupont, P., van Mourik, J.: Stability properties of Potts neural networks with biased patterns and low loading. Journal of Physics A 24, 1065–1081 (1991)
Bolle, D., Dupont, P., Huyghebaert, J.: Thermodynamics properties of the q-state Potts-glass neural network. Phys. Rew. A 45, 4194–4197 (1992)
Wu, F.Y.: The Potts model. Review of Modern Physics 54, 235–268 (1982)
Kryzhanovsky, B.V., Mikaelyan, A.L.: On the Recognizing Ability of a Neural Network on Neurons with Parametric Transformation of Frequencies. Doklady Mathematics 65(2), 286–288 (2002)
Kryzhanovsky, B.V., Litinskii, L.B., Fonarev, A.: Parametrical neural network based on the four-wave mixing process. Nuclear Instruments and Methods in Physics Research A 502(2-3), 517–519 (2003)
Kryzhanovsky, B.V., Litinskii, L.B., Mikaelyan, A.L.: Vector-neuron models of associative memory. In: Proc. of Int. Joint Conference on Neural Networks IJCNN 2004, Budapest, pp. 909–1004 (2004)
Kryzhanovsky, B.V., Mikaelyan, A.L.: An associative memory capable of recognizing strongly correlated patterns. Doklady Mathematics 67(3), 455–459 (2003)
Kryzhanovsky, B.V., Mikaelyan, A.L., Fonarev, A.B.: Vector Neural Net Identifing Many Strongly Distorted and Correlated Patterns. In: Int. conf. on Information Optics and Photonics Technology, Photonics Asia-2004, Beijing-2004. Proc. of SPIE, vol. 5642, pp. 124–133 (2004)
Kryzhanovsky, B.V., Kryzhanovsky, V.M., Mikaelian, A.L., Fonarev, A.B.: Parametrical Neural Network for Binary Patterns Identification. Optical Memory & Neural Network 14(2), 81–90 (2005)
Kryzhanovsky, B.V., Kryzhanovsky, V.M., Fonarev, A.B.: Decorrelating Parametrical Neural Network. In: Proc. of IJCNN Montreal 2005, pp. 1023–1026 (2005)
Nadal, J., Rau, A.: Storage capacity of a Potts-perceptron. J. Phys. I, France 1, 1109–1121 (1991)
Gerlf, F., Krey, U.: Storage capacity and optimal learning of Potts-model perceptrons by a cavity method. J. Phys. A: Math., Gen. 27, 7353–7372 (1994)
Kryzhanovsky, B.V., Kryzhanovsky, V.M., Magomedov, B.M., Mikaelian, A.L.: Vector Perceptron as Fast search algorithm. Optical Memory & Neural Network 13(2), 103–108 (2004)
Kryzhanovsky, V.M.: Modified q-state Potts Model with Binarized Synaptic Coefficients. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part II. LNCS, vol. 5164, pp. 72–80. Springer, Heidelberg (2008)
Alieva, D.I., Kryzhanovsky, B.V., Kryzhanovsky, V.M.: Vector-neuron models of associative memory with Clipped Synaptic Coefficiints. Pattern Recognition and Image Analysis (in press)
Kryzhanovsky, V., Kryzhanovsky, B., Fonarev, A.: Application of Potts-model Perceptron for Binary Patterns Identification. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 553–561. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kryzhanovsky, V. (2009). Pattern Identification by Committee of Potts Perceptrons. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_87
Download citation
DOI: https://doi.org/10.1007/978-3-642-04274-4_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04273-7
Online ISBN: 978-3-642-04274-4
eBook Packages: Computer ScienceComputer Science (R0)