Probabilistic Bounds for Binary Classification of Large Data Sets

0503127 - ÚI 2021 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
Kůrková, Věra - Sanguineti, M.
Probabilistic Bounds for Binary Classification of Large Data Sets.
Recent Advances in Big Data and Deep Learning. Cham: Springer, 2020 - (Oneto, L.; Navarin, N.; Sperduti, A.; Anguita, D.), s. 309-319. Proceedings of the International Neural Networks Society, 1. ISBN 978-3-030-16840-7. ISSN 2661-8141.
[INNSBDDL 2019: INNS Big Data and Deep Learning /4./. Sestri Levante (IT), 16.04.2019-18.04.2019]
Grant CEP: GA ČR(CZ) GA18-23827S
Institucionální podpora: RVO:67985807
Klíčová slova: Binary classification * Approximation by feedforward networks * Concentration of measure * Azuma-Hoeffding inequality
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Citováno: 2

--- GORBAN, A.N. - MAKAROV, V.A. - TYUKIN, I.Y. High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality. ENTROPY. JAN 2020, vol. 22, no. 1. [WOS]
--- GORBAN, A.N. - MAKAROV, V.A. - TYUKIN, I.Y. Symphony of high-dimensional brain Reply to comments on The unreasonable effectiveness of small neural ensembles in high-dimensional brain. PHYSICS OF LIFE REVIEWS. ISSN 1571-0645, JUL 2019, vol. 29, p. 115-119. [WOS]

Trvalý link: http://hdl.handle.net/11104/0294978