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- W2146677291 abstract "In this paper we present a genetic approach that uses a Bayesian fitness function to the design of rich neural network topologies in order to find an optimal domain-specific non-linear function approximator with good generalization performance. Rich neural networks have a feed-forward topology with shortcut connections and arbitrary activation functions at each layer. This kind of topologies is particularly well suited for non-linear regression tasks, but it may suffer for overfilling issues. In this paper we present a Bayesian fitness function to effectively apply genetic algorithms with these models obtaining, in a completely automated way, models well-matched to the problem, with good generalization capability, and low complexity." @default.
- W2146677291 created "2016-06-24" @default.
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- W2146677291 date "2005-02-22" @default.
- W2146677291 modified "2023-09-26" @default.
- W2146677291 title "Bayesian evolution of rich neural networks" @default.
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- W2146677291 doi "https://doi.org/10.1109/ijcnn.2004.1379904" @default.
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