Abstract
For reasoning with uncertain knowledge the use of probability theory has been broadly investigated. This paper proposed a novel probabilistic network named Bayesian-Neural Network (BNN). BNN reduces computational complexity by dividing input attribute set into two parts, each modelled by Bayesian network or Neural network. The outputs produced by different classifiers is then solved in the output space by estimating the class-conditional structural mixtures. Empirical studies on a set of natural domains show that BNN has clear advantages with respect to the generalization ability.
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Limin, W. (2006). Learning Bayesian-Neural Network from Mixed-Mode Data. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_76
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DOI: https://doi.org/10.1007/11893028_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
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