On the Computational Complexity of Learning Bithreshold Neural Units and Networks-Reference-Cited by-同舟云学术

On the Computational Complexity of Learning Bithreshold Neural Units and Networks

Published:2019-07-24 Issue: Volume: Page:189-202
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
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Author:

Kotsovsky Vladyslav^ORCID,Geche Fedir^ORCID,Batyuk Anatoliy^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-26474-1_14

Reference19 articles.

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3. Geche F, Kotsovsky V, Batyuk A, Geche S, Vashkeba M (2015) Synthesis of time series forecasting scheme based on forecasting models system. In: CEUR workshop proceedings, Lviv, Ukraine, pp 121–136

4. Vynokurova O, Peleshko D, Borzov Y, Oskerko S, Voloshyn V (2018) Hybrid multidimensional wavelet-neuro-system and its learning using cross entropy cost function in pattern recognition. In: Proceedings of the 2018 IEEE 2nd international conference on data stream mining and processing, DSMP 2018, Lviv, Ukraine, pp 305–309

5. Bovdi V, Laver V (2019) Thelma, a package on threshold elements, Version 1.02. https://gap-packages.github.io/Thelma

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