Abstract
In this paper the ability of the functional networks approach to solve classification problems is explored. Functional networks were introduced by Castillo et al. [1] as an alternative to neural networks. They have the same purpose, but unlike neural networks, neural functions are learned instead of weights, using families of linear independent functions. This is illustrated by applying several models of functional networks to a set of simulated data and to the well-known Iris data and Pima Indian data sets.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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References
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© 2005 Springer-Verlag Berlin Heidelberg
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Pruneda, R.E., Lacruz, B., Solares, C. (2005). A First Approach to Solve Classification Problems Based on Functional Networks. 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_50
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DOI: https://doi.org/10.1007/11550907_50
Publisher Name: Springer, Berlin, Heidelberg
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