Search results
- 1.0583897 - GFÚ 2024 RIV NL eng J - Journal Article
Liu, H. - Harris, J. - Sherlock, R. - Behnia, P. - Grunsky, E. - Naghizadeh, M. - Rubingh, K. - Tuba, G. - Roots, E. A. - Hill, Graham J.
Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada.
Journal of Geochemical Exploration. Roč. 253, October (2023), č. článku 107279. ISSN 0375-6742. E-ISSN 1879-1689
Institutional support: RVO:67985530
Keywords : mineral prospectivity mapping (MPM) * machine learning * partial least-squares-discriminant analysis (PLS-DA) * Random Forest (RF) * Larder Lake area
OECD category: Geology
Impact factor: 3.9, year: 2022
Method of publishing: Limited access
https://www.sciencedirect.com/science/article/abs/pii/S0375674223001267
Permanent Link: https://hdl.handle.net/11104/0351880File Download Size Commentary Version Access Liu2023JGeochemExploration.pdf 1 19.4 MB Publisher’s postprint require - 2.0583881 - ÚVGZ 2025 RIV NL eng J - Journal Article
Jevšenak, J. - Klisz, M. - Mašek, J. - Čada, V. - Janda, P. - Svoboda, M. - Vostárek, O. - Treml, V. - van der Maaten, E. - Popa, A. - Popa, I. - Van der Maaten-Theunissen, M. - Zlatanov, T. - Scharnweber, T. - Ahlgrimm, S. - Stolz, J. - Sochová, Irena - Roibu, C. C. - Pretzsch, H. - Schmied, G. - Uhl, E. - Kaczka, R. - Wrzesiński, P. - Šenfeldr, M. - Jakubowski, M. - Tumajer, J. - Wilmking, M. - Obojes, N. - Rybníček, Michal - Lévesque, M. - Potapov, A. - Basu, S. - Stojanović, Marko - Stjepanović, S. - Vitas, A. - Arnič, D. - Metslaid, S. - Neycken, A. - Prislan, P. - Hartl, C. - Ziche, D. - Horáček, Petr - Krejza, Jan - Mikhailov, Sergei - Světlík, Jan - Kalisty, A. - Kolář, Tomáš - Lavnyy, V. - Hordo, M. - Oberhuber, W. - Levanič, T. - Mészáros, I. - Schneider, L. - Lehejček, J. - Shetti, R. - Bošeľa, M. - Copini, P. - Koprowski, M. - Sass-Klaassen, U. - Izmir, Ş. C. - Bakys, R. - Entner, H. - Esper, Jan - Janecka, K. - Martinez del Castillo, E. - Verbylaite, R. - Árvai, M. - de Sauvage, J. C. - Čufar, K. - Finner, M. - Hilmers, T. - Kern, Z. - Novak, K. - Ponjarac, R. - Puchałka, R. - Schuldt, B. - Škrk Dolar, N. - Tanovski, V. - Zang, C. - Žmegač, A. - Kuithan, C. - Metslaid, M. - Thurm, E. - Hafner, P. - Krajnc, L. - Bernabei, M. - Bojić, S. - Brus, R. - Burger, A. - D'Andrea, E. - Đorem, T. - Gławęda, M. - Gričar, J. - Gutalj, M. - Horváth, E. - Kostić, S. - Matović, B. - Merela, M. - Miletić, B. - Morgós, A.
Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe.
Science of the Total Environment. Roč. 913, FEB (2024), č. článku 169692. ISSN 0048-9697. E-ISSN 1879-1026
R&D Projects: GA MŠMT LM2023048; GA TA ČR(CZ) TO01000345; GA ČR GA23-07583S
Institutional support: RVO:86652079
Keywords : ndmi * ndre * Random forest * Sentinel-1 * Sentinel-2 * Tree rings
OECD category: Remote sensing
Impact factor: 9.8, year: 2022
Method of publishing: Open access
https://www.sciencedirect.com/science/article/pii/S0048969723083225?ref=pdf_download&fr=RR-2&rr=8612eccecfc1b353
Permanent Link: https://hdl.handle.net/11104/0351873File Download Size Commentary Version Access 1-s2.0-S0048969723083225-main.pdf 5 11 MB Publisher’s postprint open-access - 3.0555130 - ÚVGZ 2023 RIV US eng J - Journal Article
Blickensdoerfer, L. - Schwieder, M. - Pflugmacher, D. - Nendel, Claas - Erasmi, S. - Hostert, P.
Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany.
Remote Sensing of Environment. Roč. 269, FEB (2022), č. článku 112831. ISSN 0034-4257. E-ISSN 1879-0704
Institutional support: RVO:86652079
Keywords : remote-sensing data * surface reflectance * estimating area * national-scale * random forest * accuracy * biodiversity * patterns * systems * Agricultural land cover * Analysis-ready data * Time series * Large-area mapping * Optical remote sensing * sar * Big data * Multi-sensor
OECD category: Agriculture
Impact factor: 13.5, year: 2022
Method of publishing: Open access
https://click.endnote.com/viewer?doi=10.1016%2Fj.rse.2021.112831&token=WzI5NjkzMTIsIjEwLjEwMTYvai5yc2UuMjAyMS4xMTI4MzEiXQ.2ErvxKTgz45sAhAIL8ihIstDfmY
Permanent Link: http://hdl.handle.net/11104/0330445 - 4.0540584 - ÚH 2022 RIV CH eng J - Journal Article
Lendzioch, T. - Langhammer, J. - Vlček, Lukáš - Minařík, R.
Mapping the groundwater level and soil moisture of a montane peat bog using UAV monitoring and machine learning.
Remote Sensing. Roč. 13, č. 5 (2021), č. článku 907. E-ISSN 2072-4292
Institutional support: RVO:67985874
Keywords : peat bog * soil moisture * UAV * machine learning (ML) * random forest (RF) * modelling
OECD category: Hydrology
Impact factor: 5.349, year: 2021
Method of publishing: Open access
https://www.mdpi.com/2072-4292/13/5/907
Permanent Link: http://hdl.handle.net/11104/0318887 - 5.0521208 - ÚVGZ 2020 RIV NL eng J - Journal Article
Turner, D. - Lucieer, A. - Malenovský, Zbyněk - King, D. - Robinson, S. A.
Assessment of Antarctic moss health from multi-sensor UAS imagery with Random Forest Modelling.
International Journal of Applied Earth Observation and Geoinformation. Roč. 68, JUN 2018 (2018), s. 168-179. ISSN 0303-2434. E-ISSN 1872-826X
Institutional support: RVO:86652079
Keywords : aerial vehicle uav * imaging spectroscopy * wilkes-land * classification * vegetation * topography * accuracy * plants * index * uav * uas * Random Forest Models * Antarctica * Moss * Multispectral * Visible * Thermal * Digital Surface Model
OECD category: Remote sensing
Impact factor: 4.846, year: 2018
Method of publishing: Open access with time embargo
https://www.sciencedirect.com/science/article/abs/pii/S0303243418300321?via%3
Permanent Link: http://hdl.handle.net/11104/0305845 - 6.0496053 - ÚVGZ 2019 RIV GB eng J - Journal Article
Heer, K. - Behringer, D. - Piermattei, A. - Baessler, C. - Brandl, R. - Fady, B. - Jehl, H. - Liepelt, S. - Lorch, S. - Piotti, A. - Vendramin, G. G. - Weller, M. - Ziegenhagen, B. - Büntgen, Ulf - Opgenoorth, L.
Linking dendroecology and association genetics in natural populations: Stress responses archived in tree rings associate with SNP genotypes in silver fir (Abies alba Mill.).
Molecular Ecology. Roč. 27, č. 6 (2018), s. 1428-1438. ISSN 0962-1083. E-ISSN 1365-294X
R&D Projects: GA MŠMT(CZ) LO1415
Institutional support: RVO:86652079
Keywords : pinus-taeda l * forest decline * candidate genes * r package * selection * drought * wide * dendrochronology * adaptation * resilience * Candidate genes * dendrophenotypes * drought stress * genetic association * random forest * SO2 pollution
OECD category: Meteorology and atmospheric sciences
Impact factor: 5.855, year: 2018
Permanent Link: http://hdl.handle.net/11104/0288866 - 7.0494114 - ÚI 2019 RIV DE eng C - Conference Paper (international conference)
Kopp, M. - Bajer, L. - Jílek, M. - Holeňa, Martin
Comparing Rule Mining Approaches for Classification with Reasoning.
ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. Aachen: Technical University & CreateSpace Independent Publishing Platform, 2018 - (Krajči, S.), s. 52-58. CEUR Workshop Proceedings, V-2203. ISSN 1613-0073.
[ITAT 2018. Conference on Information Technologies – Applications and Theory /18./. Plejsy (SK), 21.09.2018-25.09.2018]
R&D Projects: GA ČR GA17-01251S
Institutional support: RVO:67985807
Keywords : Classification * Comprehensibility * Random Forest * Rule Mining
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2203/52.pdf
Permanent Link: http://hdl.handle.net/11104/0287370File Download Size Commentary Version Access 0494114a.pdf 8 459.9 KB Publisher’s postprint require - 8.0494112 - ÚI 2019 RIV DE eng C - Conference Paper (international conference)
Pitra, Zbyněk - Repický, Jakub - Holeňa, Martin
Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy.
ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. Aachen: Technical University & CreateSpace Independent Publishing Platform, 2018 - (Krajči, S.), s. 72-79. CEUR Workshop Proceedings, V-2203. ISSN 1613-0073.
[ITAT 2018. Conference on Information Technologies – Applications and Theory /18./. Plejsy (SK), 21.09.2018-25.09.2018]
R&D Projects: GA ČR GA17-01251S
Grant - others:ČVUT(CZ) SGS17/193/OHK4/3T/14; GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : Gradient boosting * Random forest * Black-box optimization * Surrogate model * Benchmarking
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2203/72.pdf
Permanent Link: http://hdl.handle.net/11104/0287361File Download Size Commentary Version Access 0494112a.pdf 8 1.1 MB Publisher’s postprint require - 9.0493290 - ÚI 2019 RIV IE eng A - Abstract
Pitra, Zbyněk - Repický, Jakub - Holeňa, Martin
Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization.
ECML PKDD 2018: Workshop on Interactive Adaptive Learning. Proceedings. Dublin, 2018 - (Krempl, G.; Lemaire, V.; Kottke, D.; Calma, A.; Holzinger, A.; Polikar, R.; Sick, B.). s. 89-94
[ECML PKDD 2018: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 10.09.2018-14.09.2018, Dublin]
R&D Projects: GA ČR GA17-01251S
Grant - others:ČVUT(CZ) SGS17/193/OHK4/3T/14; GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : Metalearing * Surrogate model * Gaussian process * Random forest * Exploratory landscape analysis
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://www.ies.uni-kassel.de/p/ial2018/ialatecml2018.pdf
Permanent Link: http://hdl.handle.net/11104/0286679File Download Size Commentary Version Access a0493290.pdf 23 568.2 KB Sborník dostupný online. Publisher’s postprint open-access - 10.0478626 - ÚI 2018 RIV DE eng C - Conference Paper (international conference)
Puchýř, J. - Holeňa, Martin
Random-Forest-Based Analysis of URL Paths.
Proceedings ITAT 2017: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2017 - (Hlaváčová, J.), s. 129-135. CEUR Workshop Proceedings, V-1885. ISBN 978-1974274741. ISSN 1613-0073.
[ITAT 2017. Conference on Theory and Practice of Information Technologies - Applications and Theory /17./. Martinské hole (SK), 22.09.2017-26.09.2017]
R&D Projects: GA ČR GA17-01251S
Institutional support: RVO:67985807
Keywords : malicious URLs detection * classification * random forest
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-1885/129.pdf
Permanent Link: http://hdl.handle.net/11104/0274765File Download Size Commentary Version Access a0478626.pdf 1 367.6 KB Publisher’s postprint require