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- W1588837195 abstract "The author proposes a learning scheme which compensates for the incomplete result of learning using redundant internal coding of the required input-output relation and some plans to diversify inner subnetwork structures. He applies this scheme to a character recognition problem and experimentally shows that this approach gives more accurate learning results and faster convergence as well as more efficient hardware constitutions than the traditional approach. Specifically, computer simulations are presented which shows that the proposed approach is superior to the traditional approach using the so-called grandmother cell representation scheme. >" @default.
- W1588837195 created "2016-06-24" @default.
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- W1588837195 date "1991-01-01" @default.
- W1588837195 modified "2023-09-27" @default.
- W1588837195 title "A learning scheme of neural networks which improves accuracy and speed of convergence using redundant and diversified network structures" @default.
- W1588837195 cites W1971735090 @default.
- W1588837195 doi "https://doi.org/10.1109/ijcnn.1991.170584" @default.
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