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- W1491129255 abstract "Some problems of unlearning were encountered when using Fahlman's recurrent cascade correlation learning architecture (RCC) for phoneme recognition. In this paper the authors present a parallel-modular RCC. The original RCC is transformed into a modular RCC, trained with natural connectionist glue. This is done in order to concentrate the knowledge about a group of patterns in a module, instead of distributing it across the whole network. The modules are connected in parallel, in contrast to the completely cascaded structure of the original RCC. This new approach provides an improvement in the recognition rates for tasks involving large numbers of features to be learned. The modularity, besides providing a better learning, makes training of large sample-sets easier and faster." @default.
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- W1491129255 date "2002-11-19" @default.
- W1491129255 modified "2023-09-25" @default.
- W1491129255 title "A parallel recurrent cascade-correlation neural network with natural connectionist glue" @default.
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- W1491129255 doi "https://doi.org/10.1109/icnn.1995.487548" @default.
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