Martin Holeňa

发表

Dynamic classifier aggregation using interaction-sensitive fuzzy measures

David Štefka, Martin Holeňa, M. Holeňa, 2015, Fuzzy Sets Syst..

Generator approach to evolutionary optimization of catalysts and its integration with surrogate modeling

Martin Holeňa, David Linke, Uwe Rodemerck, 2011 .

THE SPECIFICITY OF NEURAL NETWORKS IN EXTRACTING RULES FROM DATA

Martin Holeňa, 2006 .

Improving Neural Network Approximations

Martin Holeňa, Manfred Baerns, M. Holeňa, 2009 .

Breaking CAPTCHAs with Convolutional Neural Networks

Martin Kopp, Martin Holeňa, Matěj Nikl, 2017, ITAT.

Object System Structure and Behaviour Analysis in Database Design

Martin Holeňa, 1988 .

Some Frequently Used Classification Methods

Martin Kopp, Martin Holeňa, Petr Pulc, 2020 .

Automatically generated problem-tailored genetic algorithms for the optimization of chemical processes

Martin Holeňa, David Linke, Uwe Rodemerck, 2006 .

Mathematical Methods of Searching for Optimal Catalytic Materials

Martin Holeňa, Manfred Baerns, M. Holeňa, 2009 .

Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy

Martin Holeňa, Jakub Repický, Lukáš Bajer, 2017, ITAT.

Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy

Martin Holeňa, Jakub Repický, Zbyněk Pitra, 2018, ITAT.

Classification Methods for Internet Applications

Martin Kopp, Martin Holeňa, Petr Pulc, 2020 .

Tuning Evolutionary Algorithms with Artificial Neural Networks

Martin Holeňa, Manfred Baerns, M. Holeňa, 2009 .

Using neural networks to tune heuristic parameters in evolutionary optimization

Martin Holeňa, M. Holeňa, 2006 .

Aiming at Predictive Accuracy

Martin Kopp, Martin Holeňa, Petr Pulc, 2020 .

Neural Networks for Extraction of Fuzzy Logic Rules with Application to EEG Data

Martin Holeňa, 2005 .

Aiming at Comprehensibility

Martin Kopp, Martin Holeňa, Petr Pulc, 2020 .

Generating Problem-Tailored Genetic Algorithms for Catalyst Search

Martin Holeňa, Manfred Baerns, 2009 .

Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings

Martin Holeňa, Tomáš Šabata, M. Holeňa, 2020, IAL@PKDD/ECML.

Artificial Neural Networks in the Development of Catalytic Materials

Martin Holeňa, Manfred Baerns, 2009 .

K-best Viterbi Semi-supervized Active Learning in Sequence Labelling

Martin Holeňa, Tomáš Šabata, Tomáš Borovička, 2017, ITAT.

Applications of Combinatorial Catalyst Development and an Outlook on Future Work

Martin Holeňa, Manfred Baerns, M. Holeňa, 2009 .

Using Past Experience for Configuration of Gaussian Processes in Black-Box Optimization

Martin Holeňa, Zbyněk Pitra, Jan Koza, 2021, LION.

Interpreting and clustering outliers with sapling random forests

Martin Holeňa, Tomáš Pevný, Martin Kopp, 2014 .

Feedforward neural networks in catalysis: A tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction

Martin Holeňa, M. Holeňa, Manfred Baerns, 2003 .