Statistical analysis of past catalytic data on oxidative methane coupling for new insights into the composition of high‐performance catalysts U Zavyalova, M Holena, R Schlögl, M Baerns ChemCatChem 3 (12), 1935-1947, 2011 | 550 | 2011 |
Catalyst Development for CO2 Hydrogenation to Fuels U Rodemerck, M Holeňa, E Wagner, Q Smejkal, A Barkschat, M Baerns ChemCatChem 5 (7), 1948-1955, 2013 | 192 | 2013 |
Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials U Rodemerck, M Baerns, M Holena, D Wolf Applied Surface Science 223 (1-3), 168-174, 2004 | 158 | 2004 |
Developing catalytic materials for the oxidative coupling of methane through statistical analysis of literature data EV Kondratenko, M Schlüter, M Baerns, D Linke, M Holena Catalysis Science & Technology 5 (3), 1668-1677, 2015 | 114 | 2015 |
The GUHA method and its meaning for data mining P Hájek, M Holeňa, J Rauch Journal of Computer and System Sciences 76 (1), 34-48, 2010 | 97 | 2010 |
Feedforward neural networks in catalysis: A tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction M Holeňa, M Baerns Catalysis Today 81 (3), 485-494, 2003 | 92 | 2003 |
Comparing middleware concepts for advanced healthcare system architectures B Blobel, M Holena International journal of medical informatics 46 (2), 69-85, 1997 | 90 | 1997 |
Revealing property-performance relationships for efficient CO2 hydrogenation to higher hydrocarbons over Fe-based catalysts: Statistical analysis of literature data and its … Q Yang, A Skrypnik, A Matvienko, H Lund, M Holena, EV Kondratenko Applied Catalysis B: Environmental 282, 119554, 2021 | 76 | 2021 |
Gaussian process surrogate models for the CMA evolution strategy L Bajer, Z Pitra, J Repický, M Holeňa Evolutionary computation 27 (4), 665-697, 2019 | 65 | 2019 |
Benchmarking Gaussian processes and random forests surrogate models on the BBOB noiseless testbed L Bajer, Z Pitra, M Holeňa Proceedings of the Companion Publication of the 2015 Annual Conference on …, 2015 | 54 | 2015 |
New catalytic materials for the high-temperature synthesis of hydrocyanic acid from methane and ammonia by high-throughput approach S Moehmel, N Steinfeldt, S Engelschalt, M Holena, S Kolf, M Baerns, ... Applied Catalysis A: General 334 (1-2), 73-83, 2008 | 54 | 2008 |
Combinatorial development of solid catalytic materials: design of high-throughput experiments, data analysis, data mining M Baerns, M Holena World Scientific, 2009 | 53 | 2009 |
Fuzzy hypotheses testing in the framework of fuzzy logic M Holeňa Fuzzy Sets and Systems 145 (2), 229-252, 2004 | 51 | 2004 |
Anomaly explanation with random forests M Kopp, T Pevný, M Holeňa Expert Systems with Applications 149, 113187, 2020 | 49 | 2020 |
An approach to structure determination and estimation of hierarchical Archimedean copulas and its application to Bayesian classification J Górecki, M Hofert, M Holeňa Journal of Intelligent Information Systems 46 (1), 21-59, 2016 | 46 | 2016 |
Fuzzy hypotheses for GUHA implications M Holeňa Fuzzy Sets and Systems 98 (1), 101-125, 1998 | 44 | 1998 |
The influence of preparation variables on the performance of Pd/Al2O3 catalyst in the hydrogenation of 1, 3-butadiene: Building a basis for reproducible catalyst synthesis T Cukic, R Kraehnert, M Holena, D Herein, D Linke, U Dingerdissen Applied Catalysis A: General 323, 25-37, 2007 | 43 | 2007 |
Optimization of magnetically driven directional solidification of silicon using artificial neural networks and Gaussian process models N Dropka, M Holena Journal of Crystal Growth 471, 53-61, 2017 | 38 | 2017 |
On structure, family and parameter estimation of hierarchical Archimedean copulas J Górecki, M Hofert, M Holeňa Journal of Statistical Computation and Simulation 87 (17), 3261-3324, 2017 | 35 | 2017 |
Surrogate model for continuous and discrete genetic optimization based on RBF networks L Bajer, M Holeňa Intelligent Data Engineering and Automated Learning–IDEAL 2010: 11th …, 2010 | 35 | 2010 |