Abstract
Parallel perceptrons (PPs), a novel approach to committee machine training requiring minimal communication between outputs and hidden units, allows the construction of efficient and stable nonlinear classifiers. In this work we shall explore how to improve their performance allowing their output weights to have real values, computed by applying Fisher’s linear discriminant analysis to the committee machine’s perceptron outputs. We shall see that the final performance of the resulting classifiers is comparable to that of the more complex and costlier to train multilayer perceptrons.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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González, A., Cantador, I., Dorronsoro, J.R. (2005). Discriminant Parallel Perceptrons. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_3
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DOI: https://doi.org/10.1007/11550907_3
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