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- W1518202215 abstract "Acquisition of internal representation by neural networks that learn identity-mappings is one of important methods for feature-abstraction. It is, however, difficult to decide the number of hidden units of the networks. In this article, I show a way to apply the overload learning (OLL) technique to overcome this problem. Because OLL causes to reduce the number of effective dimensions of hidden patterns, networks can get reduced internal representation by learning. Moreover, I show that this technique is useful to get spatial representation from symbolic representation, and to integrate various types of information." @default.
- W1518202215 created "2016-06-24" @default.
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- W1518202215 date "1995-01-01" @default.
- W1518202215 modified "2023-09-24" @default.
- W1518202215 title "Acquisition of internal representation by learning of identity-mapping using overload learning" @default.
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- W1518202215 doi "https://doi.org/10.1007/3-540-59497-3_275" @default.
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