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- W2220161617 abstract "A Bayesian neural network [2] can be seen as implementing a Bayesian classifier, which is optimal for the case of independent evidence [3]. However, the performance of a standard one-layer Bayesian network is degraded when the underlying independence assumptions are violated. One way to solve this is to turn to a multi-layer architecture, where dependencies among the inputs can be removed or, in practice, at least reduced in the internal representation." @default.
- W2220161617 created "2016-06-24" @default.
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- W2220161617 date "1993-01-01" @default.
- W2220161617 modified "2023-10-03" @default.
- W2220161617 title "A Multi-Layer Extension of a Bayesian Neural Network" @default.
- W2220161617 cites W2019165547 @default.
- W2220161617 doi "https://doi.org/10.1007/978-1-4471-2063-6_128" @default.
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