Abstract
Dynamics of training the group of single layer perceptrons aimed to solve multi-class pattern recognition problem is studied. It is shown that in special training of the perceptrons, one may obtain a pool of different classification algorithms. Means to improve training speed and reduce generalization error are studied. Training dynamics is illustrated by solving artificial multi-class pattern recognition task and important real world problem: detection of ten types of yeast infections from 1500 spectral features.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2000)
Raudys, S.: Statistical and Neural Classifiers: An integrated approach to design. Springer, Heidelberg (2001)
Raudys, Š.: Multiple classification systems in the context of feature extraction and selection. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 27–41. Springer, Heidelberg (2002)
Raudys, S.: Evolution and generalization of a single neurone. I. SLP as seven statistical classifiers. Neural Networks 11, 283–296 (1998)
Raudys, Š., Justickis, V.: Yerkes-Dodson Law in Agents’ Training. In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS (LNAI), vol. 2902, pp. 54–58. Springer, Heidelberg (2003)
Raudys, S., Amari, S.: Effect of initial values in simple perception. In: Proceedings 1998 IEEE World Congress on Computational Intelligence, IJCNN 1998, pp. 1530–1535. IEEE Press, Los Alamitos (1998)
Le Cun, Y., Kanter, I., Solla, S.: Eigenvalues of covariance matrices: application to neural-network learning. Physical Review Letters 66(18), 2396–2399 (1991)
Halkaaer, S., Winter, O.: The effect of correlated input data on the dynamics of learning. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in Neural Information Processing Systems, vol. 9, pp. 169–175. MIT Press, Cambridge (1996)
Saudargiene, A.: Structurization of the covariance matrix by process type and block diagonal models in the classifier design. Informatica 10(2), 245–269 (1999)
Raudys, S., Saudargiene, A.: First-order tree-type dependence between variables and classification performance. IEEE Trans. on Pattern Analysis and Machine Intelligence. PAMI 23(2), 233–239 (2001)
Pizzi, N.J., Pedrycz, W.: Classification of magnetic resonance spectra using parallel randomized feature selection. In: Proc. IJCNN 2004 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raudys, S., Denisov, V., Bielskis, A.A. (2006). A Pool of Classifiers by SLP: A Multi-class Case. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_5
Download citation
DOI: https://doi.org/10.1007/11867661_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44894-5
Online ISBN: 978-3-540-44896-9
eBook Packages: Computer ScienceComputer Science (R0)