Abstract
Artificial neural networks have been applied to solve hard problems in different engineering domains, thanks to their capability of universal function approximators [4]. However, when these networks are used in their standard forms, ‘black-box models’, their performances are inferior to dedicated statistical solutions. Performances can be largely improved if we introduce prior knowledge in network architectures. If the real problem has an obvious decomposition, then it may be possible to design a network architecture by hand. Unfortunately, this is not always possible.
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© 1998 Springer-Verlag Wien
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Elhor, N., Bertrand, R., Hamad, D. (1998). Bernoulli Mixture Model of Experts for Supervised Pattern Classification. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_10
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_10
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
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