Abstract
This work addresses the problem of Selecting appropriate architectures for Bayesian Neural Networks (BNN). Specifically, it proposes a variable architecture model where the number of hidden units are selected by using a variant of the real-coded Evolutionary Monte Carlo algorithm developed by Liang and Wong (2001) for inference and prediction in fixed architecture Bayesian Neural Networks.
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Bozza, S., Mantovan, P. (2006). Variable Architecture Bayesian Neural Networks: Model Selection Based on EMC. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_9
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DOI: https://doi.org/10.1007/3-540-35978-8_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35977-7
Online ISBN: 978-3-540-35978-4
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