Abstract
The paper proposes an implementation of the agent-based population learning algorithm (PLA) within the cascade correlation (CC) learning architecture. The first step of the CC procedure uses a standard learning algorithm. It is suggested that using the agent-based PLA as such an algorithm could improve efficiency of the approach. The paper gives a short overview of both – the CC algorithm and PLA, and then explains main features of the proposed agent-based PLA implementation. The approach is evaluated experimentally.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Czarnowski, I., Jędrzejowicz, P. (2005). An Agent-Based PLA for the Cascade Correlation Learning Architecture. 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_32
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DOI: https://doi.org/10.1007/11550907_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
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