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  1. 1.
    0577144 - ÚI 2024 RIV CH eng J - Journal Article
    Štěpánek, Lubomír - Dlouhá, Jana - Martinková, Patrícia
    Item Difficulty Prediction Using Item Text Features: Comparison of Predictive Performance across Machine-Learning Algorithms.
    Mathematics. Roč. 11, č. 19 (2023), č. článku 4104. ISSN 2227-7390
    R&D Projects: GA ČR(CZ) GA21-03658S
    Institutional support: RVO:67985807
    Keywords : text-based item difficulty prediction * text features and item wording * machine learning * regularization methods * elastic net regression * support vector machines * regression and decision trees * random forests * neural networks * algorithm vs. domain expert’s prediction performance
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 2.4, year: 2022
    Method of publishing: Open access
    https://dx.doi.org/10.3390/math11194104
    Permanent Link: https://hdl.handle.net/11104/0346365
    FileDownloadSizeCommentaryVersionAccess
    0577144-aoa.pdf1715.6 KBOA CC BY 4.0Publisher’s postprintopen-access
     
     
  2. 2.
    0571888 - ÚGN 2024 RIV CZ eng C - Conference Paper (international conference)
    Pecha, Marek - Langford, Z. - Horák, David - Tran Mills, R.
    Semantic segmentation using support vector machine classifier.
    Programs and Algorithms of Numerical Mathematics 21 : Proceedings of Seminar. Praha: Institute of Mathematics CAS Prague, 2023 - (Chleboun, J.; Kůs, P.; Papež, J.; Rozložník, M.; Segeth, K.; Šístek, J.), s. 173-186. ISBN 978-80-85823-73-8.
    [Programs and Algorithms of Numerical Mathematics /21./. Jablonec nad Nisou (CZ), 19.06.2022-24.06.2022]
    EU Projects: European Commission(XE) 847593 - EURAD
    Institutional support: RVO:68145535
    Keywords : wildfire identification * semantic segmentation * support vector machines * distributed training
    OECD category: Applied mathematics
    https://dml.cz/bitstream/handle/10338.dmlcz/703198/PANM_21-2022-1_19.pdf
    Permanent Link: https://hdl.handle.net/11104/0342781
    FileDownloadSizeCommentaryVersionAccess
    UGN_0571888.pdf31.4 MBOtheropen-access
     
     
  3. 3.
    0537238 - ÚGN 2021 RIV CH eng C - Conference Paper (international conference)
    Pecha, Marek - Horák, David
    Analyzing l1-loss and l2-loss Support Vector Machines Implemented in PERMON Toolbox.
    Lecture Notes in Electrical Engineering. Vol. 554. Cham: Springer Nature Switzerland AG, 2020 - (Zelinka, I.; Brandstetter, P.; Trong Dao, T.; Hoang Duy, V.; Kim, S.), s. 13-23. ISBN 978-3-030-14906-2. ISSN 1876-1100. E-ISSN 1876-1119.
    [International Conference on Advanced Engineering Theory and Applications 2018 /5./. Ostrava (CZ), 11.11.2018-13.11.2018]
    R&D Projects: GA MŠMT LQ1602
    Institutional support: RVO:68145535
    Keywords : Support Vector Machines * PermonSVM * Hinge loss functions * quadratic programming * MPRGP
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    https://link.springer.com/chapter/10.1007/978-3-030-14907-9_2
    Permanent Link: http://hdl.handle.net/11104/0314975
     
     
  4. 4.
    0522793 - ÚI 2021 RIV CH eng B - Monography
    Holeňa, Martin - Pulc, P. - Kopp, M.
    Classification Methods for Internet Applications.
    Springer: Cham, 2020. 281 s. Studies in Big Data, 69. ISBN 978-3-030-36961-3
    Institutional support: RVO:67985807
    Keywords : Spam filtering * Recommender systems * Malware detection * Network intrusion detection * Random forests * Classifier comprehensibility * Support vector machines * Nearest neighbours classification * Bayesian classifiers
    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/11104/0307224
     
     
  5. 5.
    0495870 - ÚGN 2019 RIV CH eng C - Conference Paper (international conference)
    Kružík, Jakub - Pecha, Marek - Hapla, D. - Horák, David - Čermák, Martin
    Investigating convergence of linear SVM implemented in PermonSVM employing MPRGP algorithm.
    High Performance Computing in Science and Engineering. HPCSE 2017. Cham: Springer, 2018 - (Kozubek, T.), s. 115-129. Lecture Notes in Computer Science, Code 216349, Volume 11087. ISBN 978-3-319-97135-3.
    [HPCSE 2017: International Conference on High Performance Computing in Science and Engineering /3./. Karolinka (CZ), 22.05.2017-25.05.2017]
    R&D Projects: GA MŠMT LQ1602
    Grant - others:Ga MŠk(CZ) LM2015070; GA ČR(CZ) GA15-18274S
    Institutional support: RVO:68145535
    Keywords : MPRGP * PERMON * PermonQP * PermonSVM * quadratic programming * support vector machines
    OECD category: Applied mathematics
    https://link.springer.com/chapter/10.1007/978-3-319-97136-0_9
    Permanent Link: http://hdl.handle.net/11104/0288753
    FileDownloadSizeCommentaryVersionAccess
    UGN_0495870.pdf21.1 MBAuthor’s postprintrequire
     
     
  6. 6.
    0490053 - ÚI 2019 RIV GB eng J - Journal Article
    Mikoláš, P. - Hlinka, Jaroslav - Škoch, A. - Pitra, Zbyněk - Frodl, T. - Španiel, F. - Hájek, T.
    Machine Learning Classification of First-Episode Schizophrenia Spectrum Disorders and Controls Using Whole Brain White Matter Fractional Anisotropy.
    BMC Psychiatry. Roč. 18, 10 April (2018), č. článku 97. E-ISSN 1471-244X
    R&D Projects: GA ČR GA17-01251S
    Grant - others:GA MŠk(CZ) LO1611; GA MZd(CZ) NV16-32696A
    Institutional support: RVO:67985807
    Keywords : First-episode schizophrenia spectrum disorders * Diffusion tensor imaging * Support vector machines * Magnetic resonance imaging
    OECD category: Neurosciences (including psychophysiology
    Impact factor: 2.666, year: 2018
    Permanent Link: http://hdl.handle.net/11104/0284354
    FileDownloadSizeCommentaryVersionAccess
    a0490053.pdf111.6 MBPublisher’s postprintopen-access
     
     
  7. 7.
    0475094 - ÚI 2018 US eng A - Abstract
    Mikoláš, P. - Hlinka, Jaroslav - Pitra, Z. - Škoch, A. - Frodl, T. - Španiel, F. - Hájek, T.
    Classification of First-Episode Schizophrenia Spectrum Disorders and Controls from Whole Brain White Matter Fractional Anisotropy Using Machine Learning.
    Biological Psychiatry. Elsevier. Roč. 81, č. 10 (2017), S252-S252. ISSN 0006-3223. E-ISSN 1873-2402.
    [Annual Scientific Convention and Meeting of the Society-of-Biological-Psychiatry /72./. 18.05.2017-20.05.2017, San Diego]
    Grant - others:GA MŠk(CZ) LO1611
    Institutional support: RVO:67985807
    Keywords : first episode schizophrenia * machine learning * fractional anisotropy * diffusion tensor imaging (DTI) * support vector machines
    http://www.sciencedirect.com/science/article/pii/S0006322317306145
    Permanent Link: http://hdl.handle.net/11104/0271967
    FileDownloadSizeCommentaryVersionAccess
    a0475094.pdf036.8 KBPublisher’s postprintopen-access
     
     
  8. 8.
    0421353 - ÚFP 2014 RIV US eng J - Journal Article
    Odstrčil, Michal - Murari, A. - Mlynář, Jan
    Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies.
    IEEE Transactions on Plasma Science. Roč. 41, č. 7 (2013), s. 1751-1759. ISSN 0093-3813. E-ISSN 1939-9375
    R&D Projects: GA ČR GAP205/10/2055
    Institutional support: RVO:61389021
    Keywords : Learning Machines * Support Vector Machines * Neural Network * ASDEX Upgrade * JET * Disruption mitigation * Tokamaks * ITER
    Subject RIV: BL - Plasma and Gas Discharge Physics
    Impact factor: 0.950, year: 2013
    Permanent Link: http://hdl.handle.net/11104/0227726
     
     
  9. 9.
    0398102 - ÚPT 2014 RIV SK eng C - Conference Paper (international conference)
    Mikulka, J. - Bartušek, Karel - Dvořák, P.
    Support Vector Machines in MR Images Segmentation.
    Measurement 2013. Proceedings of the 9th International Conference on Measurement. Bratislava: Institute of Measurement Science SAS, 2013, s. 157-160. ISBN 978-80-969672-5-4.
    [Measurement 2013. International Conference on Measurement /9./. Smolenice (SK), 27.05.2013-30.05.2013]
    R&D Projects: GA ČR GAP102/12/1104; GA MŠMT ED0017/01/01
    Institutional support: RVO:68081731
    Keywords : perfusion analysis * brain tumor segmentation * data classification * support vector machines * multi-parametric segmentation
    Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering
    Permanent Link: http://hdl.handle.net/11104/0225647
     
     
  10. 10.
    0095815 - ÚI 2008 RIV US eng C - Conference Paper (international conference)
    Holeňa, Martin - Moravec, J.
    Combining Support Vector Machines by Means of Fuzzy Aggregation.
    [Kombinování klasifikátorů založených na opěrných vektorech pomocí fuzzy agregace.]
    Selected Topics on Circuits, Systems, Electronics, Control and Signal Processing. -: WSEAS Press, 2007, s. 130-135. Mathematics and Computers in Science and Engineering. ISBN 978-960-6766-28-2.
    [CSECS'07. WSEAS International Conference on Circuits, Systems, Electronics, Control and Signal Processing /6./. Cairo (EG), 29.12.2007-31.12.2007]
    R&D Projects: GA MŠMT ME 701
    Institutional research plan: CEZ:AV0Z10300504
    Keywords : support vector machines * fuzzy aggregation * t-conorm integral * qiuasi-Sugeno integral
    Subject RIV: IN - Informatics, Computer Science
    Permanent Link: http://hdl.handle.net/11104/0155318
     
     

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