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- W213826100 abstract "Modular neural networks (MNNs) are increasingly popular models for dealing with complex problems constituted by a number of dependent subtasks. An important problem on MNNs is finding the optimal aggregation of the neural modules, each of them dealing with one of the subproblems. In this paper, we present a functional network approach, based on the minimum description length quality measure, to the problem of finding optimal modular network architectures for specific problems. Examples of function approximation and nonlinear time series prediction are used to illustrate the performance of these models when compared with standard functional and neural networks." @default.
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- W213826100 date "2001-10-01" @default.
- W213826100 modified "2023-10-09" @default.
- W213826100 title "Minimum description length quality measurues for modular functional network architectures" @default.
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