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- 1.0493061 - ÚI 2019 RIV SG eng J - Journal Article
Vidnerová, Petra - Neruda, Roman
Kernel Function Tuning for Single-Layer Neural Networks.
International Journal of Machine Learning and Computing. Roč. 8, č. 4 (2018), s. 354-360. ISSN 2010-3700
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : radial basis function networks * shallow neural networks * kernel methods * hyper-parameter tuning
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=79&id=831
Permanent Link: http://hdl.handle.net/11104/0286524File Download Size Commentary Version Access a0493061.pdf 7 1.3 MB OA Publisher’s postprint open-access - 2.0485639 - ÚI 2021 RIV GB eng J - Journal Article
Vidnerová, Petra - Neruda, Roman
Vulnerability of classifiers to evolutionary generated adversarial examples.
Neural Networks. Roč. 127, July (2020), s. 168-181. ISSN 0893-6080. E-ISSN 1879-2782
R&D Projects: GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : supervised learning * neural networks * kernel methods * genetic algorithms * adversarial examples
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 8.050, year: 2020
Method of publishing: Limited access
http://dx.doi.org/10.1016/j.neunet.2020.04.015
Permanent Link: http://hdl.handle.net/11104/0280599 - 3.0485613 - ÚI 2020 RIV US eng J - Journal Article
Kůrková, Věra
Limitations of Shallow Networks Representing Finite Mappings.
Neural Computing & Applications. Roč. 31, č. 6 (2019), s. 1783-1792. ISSN 0941-0643. E-ISSN 1433-3058
R&D Projects: GA ČR GA15-18108S; GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : shallow and deep networks * sparsity * variational norms * functions on large finite domains * finite dictionaries of computational units * pseudo-noise sequences * perceptron networks
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 4.774, year: 2019
Method of publishing: Open access
http://dx.doi.org/10.1007/s00521-018-3680-1
Permanent Link: http://hdl.handle.net/11104/0280569File Download Size Commentary Version Access 0485613-afin.pdf 12 608 KB stránkovaná, finální verze Publisher’s postprint require 0485613.pdf 5 330.3 KB Author´s preprint require - 4.0485611 - ÚI 2020 RIV US eng J - Journal Article
Kůrková, Věra - Sanguineti, M.
Classification by Sparse Neural Networks.
IEEE Transactions on Neural Networks and Learning Systems. Roč. 30, č. 9 (2019), s. 2746-2754. ISSN 2162-237X. E-ISSN 2162-2388
R&D Projects: GA ČR GA15-18108S; GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : Binary classification * Chernoff–Hoeffding bound * dictionaries of computational units * feedforward networks * measures of sparsity
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 8.793, year: 2019
Method of publishing: Limited access
http://dx.doi.org/10.1109/TNNLS.2018.2888517
Permanent Link: http://hdl.handle.net/11104/0280566File Download Size Commentary Version Access 0485611-a.pdf 18 458.9 KB Publisher’s postprint require - 5.0474092 - ÚI 2019 RIV US eng J - Journal Article
Kůrková, Věra
Constructive Lower Bounds on Model Complexity of Shallow Perceptron Networks.
Neural Computing & Applications. Roč. 29, č. 7 (2018), s. 305-315. ISSN 0941-0643. E-ISSN 1433-3058
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : shallow and deep networks * model complexity and sparsity * signum perceptron networks * finite mappings * variational norms * Hadamard matrices
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 4.664, year: 2018
Permanent Link: http://hdl.handle.net/11104/0271209File Download Size Commentary Version Access a0474092.pdf 8 495.8 KB Publisher’s postprint require - 6.0473964 - ÚI 2018 RIV GB eng J - Journal Article
Kůrková, Věra - Sanguineti, M.
Probabilistic Lower Bounds for Approximation by Shallow Perceptron Networks.
Neural Networks. Roč. 91, July (2017), s. 34-41. ISSN 0893-6080. E-ISSN 1879-2782
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : shallow networks * perceptrons * model complexity * lower bounds on approximation rates * Chernoff-Hoeffding bounds
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 7.197, year: 2017
Permanent Link: http://hdl.handle.net/11104/0271067File Download Size Commentary Version Access a0473964.pdf 16 549.9 KB Publisher’s postprint require