0546161 - ÚI 2022 RIV DE eng C - Conference Paper (international conference)
Tumpach, Jiří - Kalina, Jan - Holeňa, MartinA Comparison of Regularization Techniques for Shallow Neural Networks Trained on Small Datasets.
Proceedings of the 21st Conference Information Technologies – Applications and Theory (ITAT 2021). Aachen: Technical University & CreateSpace Independent Publishing, 2021 - (Brejová, B.; Ciencialová, L.; Holeňa, M.; Mráz, F.; Pardubská, D.; Plátek, M.; Vinař, T.), s. 94-103. ISSN 1613-0073.
[ITAT 2021: Information Technologies - Applications and Theory /21./. Heľpa (SK), 24.09.2021-28.09.2021]
R&D Projects: GA ČR(CZ) GA18-18080S; GA ČR(CZ) GA19-05704S
Grant - others:Ministerstvo školství, mládeže a tělovýchovy - GA MŠk(CZ) LM2018140
Institutional support: RVO:67985807
Keywords : artificial neural networks * regularization * robustness * optimization
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper38.pdf
Permanent Link: http://hdl.handle.net/11104/0322710
