Výsledky vyhledávání
- 1.0573674 - ÚI 2024 RIV SK eng C - Konferenční příspěvek (zahraniční konf.)
Hlaváčková-Schindler, Kateřina - Pacher, C. - Plant, C. - Lazarenko, M. - Paluš, Milan - Hlinka, Jaroslav - Kathpalia, Aditi - Brunovský, M.
Pattern Discovery in an EEG Database of Depression Patients: Preliminary Results.
2023 14th International Conference on Measurement. Proceedings. Bratislava: Institute of Measurement Science, SAS / IEEE, 2023 - (Dvurečenskij, A.; Maňka, J.; Švehlíková, J.; Witkovský, V.), s. 80-83. ISBN 979-8-3503-1218-8.
[MEASUREMENT 2023: International Conference on Measurement /14./. Smolenice (SK), 29.05.2023-31.05.2023]
Grant CEP: GA ČR(CZ) GF21-14727K
Institucionální podpora: RVO:67985807
Klíčová slova: Major Depressive Disorder * Interactive Clustering * Granger Causality * Classification Methods
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://dx.doi.org/10.23919/MEASUREMENT59122.2023.10164584
Trvalý link: https://hdl.handle.net/11104/0344055 - 2.0569861 - ÚI 2023 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Jalaldoust, A. - Hlaváčková-Schindler, Kateřina - Plant, C.
Causal Discovery in Hawkes Processes by Minimum Description Length.
Proceedings of the 36th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2022, s. 6978-6987. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36 No. 6: AAAI-22 Technical Tracks 6. ISBN 978-1-57735-876-3. ISSN 2159-5399. E-ISSN 2374-3468.
[The AAAI Conference on Artificial Intelligence /36./. Online (US), 22.02.2022-01.03.2022]
Grant CEP: GA ČR(CZ) GA19-16066S
Institucionální podpora: RVO:67985807
Klíčová slova: Granger causality * minimum description length principle * probability distributions * Hawkes process
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://ojs.aaai.org/index.php/AAAI/article/view/20656/20415
Trvalý link: https://hdl.handle.net/11104/0341202 - 3.0567047 - ÚI 2023 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Balazia, M. - Hlaváčková-Schindler, Kateřina - Sojka, P. - Plant, C.
Interpretable Gait Recognition by Granger Causality.
2022 26th International Conference on Pattern Recognition (ICPR). Piscataway: IEEE, 2022, s. 1069-1075. ISBN 978-1-6654-9063-4. ISSN 1051-4651.
[ICPR 2022: International Conference on Pattern Recognition /26./. Montréal (CA), 21.08.2022-25.08.2022]
Grant CEP: GA ČR(CZ) GA19-16066S
Institucionální podpora: RVO:67985807
Klíčová slova: Measurement * Analytical models * Three-dimensional displays * Neural networks * Video surveillance * Skeleton * Motion capture
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://dx.doi.org/10.1109/ICPR56361.2022.9956624
Trvalý link: https://hdl.handle.net/11104/0338380 - 4.0539725 - ÚI 2022 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
Hlaváčková-Schindler, Kateřina - Plant, C.
Poisson Graphical Granger Causality by Minimum Message Length.
Machine Learning and Knowledge Discovery in Databases. Proceedings, Part 1. Cham: Springer, 2021 - (Hutter, F.; Kersting, K.; Lijffijt, J.; Valera, I.), s. 526-541. Lecture Notes in Artificial Intelligence, 12457. ISBN 978-3-030-67657-5. ISSN 0302-9743.
[ECML PKDD 2020: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Ghent / Virtual (BE), 14.09.2020-18.09.2020]
Grant CEP: GA ČR(CZ) GA19-16066S
Institucionální podpora: RVO:67985807
Klíčová slova: Granger causality * Poisson graphical Granger model * Minimum message length * Ridge regression for GLM
Obor OECD: Applied mathematics
Trvalý link: http://hdl.handle.net/11104/0317427Název souboru Staženo Velikost Komentář Verze Přístup 0539725-apre.pdf 1 458.1 KB Autorský preprint vyžádat - 5.0505979 - ÚI 2020 CH eng C - Konferenční příspěvek (zahraniční konf.)
Behzadi, S. - Hlaváčková-Schindler, Kateřina - Plant, C.
Granger causality for heterogeneous processes.
Advances in Knowledge Discovery and Data Mining. Cham: Springer, 2019 - (Qiang; Yang, Q.; Zhou, Z.; Gong, Z.; Zhang, M.; Huang, S.), s. 463-475. Lecture Notes in Artificial Intelligence, 11441. ISBN 978-3-030-16141-5. ISSN 0302-9743.
[PAKDD 2019. Pacific-Asia Conference on Knowledge Discovery and Data Mining /23./. Macau (CN), 14.04.2019-17.04.2019]
Trvalý link: http://hdl.handle.net/11104/0297299 - 6.0504995 - ÚI 2020 US eng C - Konferenční příspěvek (zahraniční konf.)
Behzadi, S. - Hlaváčková-Schindler, Kateřina - Plant, C.
Dependency anomaly detection for heterogeneous time series: A Granger-Lasso approach.
17th IEEE International Conference on Data Mining Workshops. Piscataway: IEEE, 2017, s. 1090-1099. ISBN 978-1-5386-1480-8. ISSN 2375-9259.
[ICDM 2017. IEEE International Conference onData Mining Workshops. New Orleans (US), 18.11.2017-21.11.2017]
Trvalý link: http://hdl.handle.net/11104/0296523