Search results

  1. 1.
    0572576 - ÚI 2024 RIV GB eng J - Journal Article
    Kůrková, Věra - Sanguineti, M.
    Approximation of Classifiers by Deep Perceptron Networks.
    Neural Networks. Roč. 165, August 2023 (2023), s. 654-661. ISSN 0893-6080. E-ISSN 1879-2782
    R&D Projects: GA ČR(CZ) GA22-02067S
    Institutional support: RVO:67985807
    Keywords : Approximation by deep networks * Probabilistic bounds on approximation errors * Random classifiers * Concentration of measure * Method of bounded differences * Growth functions
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 7.8, year: 2022
    Method of publishing: Limited access
    https://dx.doi.org/10.1016/j.neunet.2023.06.004
    Permanent Link: https://hdl.handle.net/11104/0343221
     
     
  2. 2.
    0549873 - ÚI 2023 RIV CH eng M - Monography Chapter
    Kůrková, Věra
    Some Implications of Interval Approach to Dimension for Network Complexity.
    Computational Intelligence and Mathematics for Tackling Complex Problems 2. Cham: Springer, 2022 - (Cornejo, M.; Kóczy, L.; Medina-Moreno, J.; Moreno-García, J.), s. 113-119. Studies in Computational Intelligence, 955. ISBN 978-3-030-88816-9
    R&D Projects: GA ČR(CZ) GA18-23827S
    Institutional support: RVO:67985807
    Keywords : Quasiorthogonal dimension * Sparsity of feedforward networks * High-dimensional geometry * Concentration of measure * Covering numbers
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Permanent Link: http://hdl.handle.net/11104/0325766
     
     
  3. 3.
    0543168 - ÚI 2022 RIV DE eng J - Journal Article
    Kůrková, Věra - Sanguineti, M.
    Correlations of Random Classifiers on Large Data Sets.
    Soft Computing. Roč. 25, č. 19 (2021), s. 12641-12648. ISSN 1432-7643. E-ISSN 1433-7479
    R&D Projects: GA ČR(CZ) GA19-05704S
    Institutional support: RVO:67985807
    Keywords : Random classifiers * Optimization of feedforward networks * Binary classification * Concentration of measure * Method of bounded differences
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 3.732, year: 2021
    Method of publishing: Limited access
    http://dx.doi.org/10.1007/s00500-021-05938-4
    Permanent Link: http://hdl.handle.net/11104/0320443
     
     
  4. 4.
    0510711 - ÚI 2020 ES eng A - Abstract
    Kůrková, Věra
    Some Implications of Interval Approach to Dimension for Network Complexity.
    ESCIM 2019. Book of Abstracts. Cádiu: University of Cádiz, 2019 - (Kóczy, L.; Medina, J.). s. 59-60. ISBN 978-84-09-14600-0.
    [ESCIM 2019: European Symposium on Computational Intelligence and Mathematics /11./. 02.10.2019-05.10.2019, Toledo]
    R&D Projects: GA ČR(CZ) GA18-23827S
    Institutional support: RVO:67985807
    Keywords : quasiorthogonal dimension * sparsity of feedforward networks * high-dimensional geometry * concentration of measure * covering numbers
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Permanent Link: http://hdl.handle.net/11104/0301114
    FileDownloadSizeCommentaryVersionAccess
    0510711-a.pdf1120.6 KBPublisher’s postprintrequire
     
     
  5. 5.
    0507969 - ÚI 2020 RIV CH eng C - Conference Paper (international conference)
    Kůrková, Věra
    Probabilistic Bounds for Approximation by Neural Networks.
    Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. Proceedings, Part I. Cham: Springer, 2019 - (Tetko, I.; Kůrková, V.; Karpov, P.; Theis, F.), s. 418-428. Lecture Notes in Computer Science, 11727. ISBN 978-3-030-30486-7. ISSN 0302-9743.
    [ICANN 2019. International Conference on Artificial Neural Networks /28./. Munich (DE), 17.09.2019-19.09.2019]
    R&D Projects: GA ČR(CZ) GA19-05704S
    Institutional support: RVO:67985807
    Keywords : Approximation of random functions * Feedforward networks * Dictionaries of computational units * High-dimensional geometry * Concentration of measure * Azuma-Hoeffding inequalities
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Permanent Link: http://hdl.handle.net/11104/0298932
     
     
  6. 6.
    0503896 - ÚI 2020 RIV NL eng J - Journal Article
    Kůrková, Věra
    Some insights from high-dimensional spheres: Comment on 'The unreasonable effectiveness of small neural ensembles in high-dimensional brain' by Alexander N. Gorban et al.
    Physics of Life Reviews. Roč. 29, July 2019 (2019), s. 98-100. ISSN 1571-0645. E-ISSN 1873-1457
    R&D Projects: GA ČR(CZ) GA18-23827S
    Institutional support: RVO:67985807
    Keywords : neural networks * high-dimensional geometry * concentration of measure * commentary
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 14.789, year: 2019
    Method of publishing: Limited access
    Permanent Link: http://hdl.handle.net/11104/0295662
     
     
  7. 7.
    0503127 - ÚI 2021 RIV CH eng C - Conference Paper (international conference)
    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]
    R&D Projects: GA ČR(CZ) GA18-23827S
    Institutional support: RVO:67985807
    Keywords : Binary classification * Approximation by feedforward networks * Concentration of measure * Azuma-Hoeffding inequality
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Permanent Link: http://hdl.handle.net/11104/0294978
    FileDownloadSizeCommentaryVersionAccess
    0503127a.pdf6164.7 KBPublisher’s postprintrequire
     
     
  8. 8.
    0476509 - ÚI 2018 RIV CH eng C - Conference Paper (international conference)
    Kůrková, Věra
    Sparsity of Shallow Networks Representing Finite Mappings.
    EANN 2017. Cham: Springer, 2017 - (Boracchi, G.; Iliadis, L.; Jayne, C.; Likas, A.), s. 337-348. Communications in Computer and Information Science, 744. ISBN 978-3-319-65171-2. ISSN 1865-0929.
    [EANN 2017. International Conference /18./. Athens (GR), 25.08.2017-27.08.2017]
    R&D Projects: GA ČR GA15-18108S
    Institutional support: RVO:67985807
    Keywords : shallow networks * finite mappings * sparsity * model complexity * concentration of measure * signum perceptrons
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Permanent Link: http://hdl.handle.net/11104/0272989
    FileDownloadSizeCommentaryVersionAccess
    a0476509.pdf3229.6 KBPublisher’s postprintrequire
     
     
  9. 9.
    0449922 - ÚI 2016 RIV SK cze C - Conference Paper (international conference)
    Kůrková, Věra
    Modelová složitost neuronových sítí - zdánlivý paradox.
    [Model Complexity of Neural Networks - a Seeming Paradox.]
    Kognícia a umelý život 2015. Bratislava: Univerzita Komenského v Bratislave, 2015 - (Farkaš, I.; Takáč, M.; Rybár, J.; Kelemen, J.), s. 102-106. ISBN 978-80-223-3875-2.
    [Kognícia a umelý život /15./. Trenčianske Teplice (SK), 25.05.2015-28.05.2015]
    R&D Projects: GA MŠMT(CZ) LD13002
    Institutional support: RVO:67985807
    Keywords : model complexity of feedforward neural networks * one-hidden-layer networks * concentration of measure
    Subject RIV: IN - Informatics, Computer Science
    http://cogsci.fmph.uniba.sk/kuz2015/zbornik/prispevky/kurkova.pdf
    Permanent Link: http://hdl.handle.net/11104/0251322
    FileDownloadSizeCommentaryVersionAccess
    a0449922.pdf22.2 MBPublisher’s postprintrequire
    0449922.pdf1762.7 KBAuthor´s preprintopen-access
     
     


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