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.
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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
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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
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