Martin Holena

ORCID

Publication Activity (10 Years)

Years Active: 1994-2024
Publications (10 Years): 59

Top Venues

Publications

Marek Dedic, Lukás Bajer, Pavel Procházka, Martin Holena
Balancing performance and complexity with adaptive graph coarsening. Tiny Papers @ ICLR (2024)
Uladzislau Yorsh, Martin Holena, Ondrej Bojar, David Herel
On Difficulties of Attention Factorization through Shared Memory. Tiny Papers @ ICLR (2024)
Lukás Korel, Alexander S. Behr, Norbert Kockmann, Martin Holena
Using Paraphrasers to Detect Duplicities in Ontologies. KEOD (2023)
Lukás Korel, Alexander S. Behr, Norbert Kockmann, Martin Holena
Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts. CoRR (2023)
Jirí Tumpach, Jan Koza, Martin Holena
Improving Optimization with Gaussian Processes in the Covariance Matrix Adaptation Evolution Strategy. ITAT (2023)
Lukás Korel, Petr Pulc, Jirí Tumpach, Martin Holena
Video Scene Location Recognition with Neural Networks. CoRR (2023)
Lukás Korel, Uladzislau Yorsh, Alexander S. Behr, Norbert Kockmann, Martin Holena
Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis. Comput. 12 (1) (2023)
Ayyub Sheikhi, Radko Mesiar, Martin Holena
A dimension reduction in neural network using copula matrix. Int. J. Gen. Syst. 52 (2) (2023)
Zbynek Pitra, Jan Koza, Jirí Tumpach, Martin Holena
Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context. CoRR (2022)
Jan Kalina, Jirí Tumpach, Martin Holena
On Combining Robustness and Regularization in Training Multilayer Perceptrons over Small Data. IJCNN (2022)
Jirí Tumpach, Jan Koza, Martin Holena
Neural-network-based estimation of normal distributions in black-box optimization. ESANN (2022)
Lukás Korel, Alexander Behr, Norbert Kockmann, Martin Holena
Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts. ITAT (2022)
Uladzislau Yorsh, Alexander Behr, Norbert Kockmann, Martin Holena
Text-to-Ontology Mapping via Natural Language Processing Models. ITAT (2022)
Jan Koza, Jirí Tumpach, Zbynek Pitra, Martin Holena
Using Past Experience for Configuration of Gaussian Processes in Black-Box Optimization. LION (2021)
Jirí Tumpach, Jan Kalina, Martin Holena
A Comparison of Regularization Techniques for Shallow Neural NetworksTrained on Small Datasets. ITAT (2021)
Lukás Korel, Petr Pulc, Jirí Tumpach, Martin Holena
Video Scene Location Recognition with Neural Networks. ITAT (2021)
Jan Koza, Jirí Tumpach, Zbynek Pitra, Martin Holena
Combining Gaussian Processes and Neural Networks in Surrogate Modelling for Covariance Matrix Adaptation Evolution Strategy. ITAT (2021)
Sergej Borisov, Marek Dedic, Martin Holena
Experimental Investigation of Neural and Weisfeiler-Lehman-Kernel Graph Representations for Downstream Classification. ITAT (2021)
Zbynek Pitra, Marek Hanus, Jan Koza, Jirí Tumpach, Martin Holena
Interaction between model and its evolution control in surrogate-assisted CMA evolution strategy. GECCO (2021)
Jirí Ruzicka, Jan Koza, Jirí Tumpach, Zbynek Pitra, Martin Holena
Combining Gaussian Processes with Neural Networks for Active Learning in Optimization. IAL@PKDD/ECML (2021)
Petr Pulc, Martin Holena
Unsupervised Construction of Task-Specific Datasets for Object Re-identification. ICCTA (2021)
Zbynek Pitra, Martin Holena
Towards Landscape Analysis in Adaptive Learning of Surrogate Models. IAL@PKDD/ECML (2020)
Marek Dedic, Tomás Pevný, Lukás Bajer, Martin Holena
Loss Functions for Clustering in Multi-instance Learning. ITAT (2020)
Jan Koza, Marek Krcál, Martin Holena
Two Semi-supervised Approaches to Malware Detection with Neural Networks. ITAT (2020)
Jirí Tumpach, Martin Holena
Online Malware Detection with Variational Autoencoders. ITAT (2020)
Mikulás Dvorák, Zbynek Pitra, Martin Holena
Assessment of Surrogate Model Settings Using Landscape Analysis. ITAT (2020)
Radek Busa, Yann Dauxais, Stefan Ecklebe, Natasha Dropka, Martin Holena
Extraction of Classification Rules from Sequences of Crystal Growth Data. ITAT (2020)
Tomás Sabata, Martin Holena
Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings. IAL@PKDD/ECML (2020)
Martin Kopp, Tomás Pevný, Martin Holena
Anomaly explanation with random forests. Expert Syst. Appl. 149 (2020)
Matej Fanta, Petr Pulc, Martin Holena
Rules Extraction from Neural Networks Trained on Multimedia Data. ITAT (2019)
Lukás Bajer, Zbynek Pitra, Jakub Repický, Martin Holena
Gaussian process surrogate models for the CMA-ES. GECCO (Companion) (2019)
Zbynek Pitra, Jakub Repický, Martin Holena
Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy. GECCO (2019)
Lukás Bajer, Zbynek Pitra, Jakub Repický, Martin Holena
Gaussian Process Surrogate Models for the CMA Evolution Strategy. Evol. Comput. 27 (4) (2019)
Jirí Tumpach, Marek Krcál, Martin Holena
Deep Networks in Online Malware Detection. ITAT (2019)
Martin Kopp, Marek Jílek, Martin Holena
Comparing rule mining approaches for classification with reasoning. ITAT (2018)
Oliver Kerul-Kmec, Petr Pulc, Martin Holena
Semisupervised Segmentation of UHD Video. ITAT (2018)
Zbynek Pitra, Jakub Repický, Martin Holena
Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy. ITAT (2018)
Jiri Kozusznik, Petr Pulc, Martin Holena
Sentiment Analysis from Utterances. ITAT (2018)
Petr Pulc, Martin Holena
Hierarchical Motion Tracking Using Matching of Sparse Features. SITIS (2018)
Tomás Sabata, Petr Pulc, Martin Holena
Semi-supervised and Active Learning in Video Scene Classification from Statistical Features. IAL@PKDD/ECML (2018)
Jakub Repický, Martin Holena, Zbynek Pitra
Automated Selection of Covariance Function for Gaussian process Surrogate Models. ITAT (2018)
Zbynek Pitra, Jakub Repický, Martin Holena
Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization. IAL@PKDD/ECML (2018)
Jakub Repický, Zbynek Pitra, Martin Holena
Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization. IAL@PKDD/ECML (2018)
Jakub Repický, Lukás Bajer, Zbynek Pitra, Martin Holena
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models. ITAT (2017)
Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena
Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy. ITAT (2017)
Martin Kopp, Matej Nikl, Martin Holena
Breaking CAPTCHAs with Convolutional Neural Networks. ITAT (2017)
Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena
Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy. GECCO (Companion) (2017)
Jakub Repický, Lukás Bajer, Zbynek Pitra, Martin Holena
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models. CoRR (2017)
Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena
Overview of surrogate-model versions of covariance matrix adaptation evolution strategy. GECCO (Companion) (2017)
Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena
Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy. GECCO (Companion) (2017)
Jakub Puchýr, Martin Holena
Random-Forest-Based Analysis of URL Paths. ITAT (2017)
Tomás Sabata, Tomás Borovicka, Martin Holena
K-best Viterbi Semi-supervized Active Learning in Sequence Labelling. ITAT (2017)
Zbynek Pitra, Lukás Bajer, Martin Holena
Doubly Trained Evolution Control for the Surrogate CMA-ES. PPSN (2016)
Martin Kopp, Matous Pistora, Martin Holena
How to Mimic Humans, Guide for Computers. ITAT (2016)
Jakub Repický, Lukás Bajer, Martin Holena
Traditional Gaussian Process Surrogates in the BBOB Framework. ITAT (2016)
Petr Pulc, Erec Rosenzveig, Martin Holena
Image Processing in Collaborative Open Narrative Systems. ITAT (2016)
Nikita Orekhov, Lukás Bajer, Martin Holena
Testing Gaussian Process Surrogates on CEC'2013 Multi-Modal Benchmark. ITAT (2016)
Tomás Sabata, Tomás Borovicka, Martin Holena
Modeling and Clustering the Behavior of Animals Using Hidden Markov Models. ITAT (2016)
Jan Gorecki, Marius Hofert, Martin Holena
An approach to structure determination and estimation of hierarchical Archimedean Copulas and its application to Bayesian classification. J. Intell. Inf. Syst. 46 (1) (2016)
Lukás Bajer, Martin Holena
Model Guided Sampling Optimization for Low-dimensional Problems. CoRR (2015)
Martin Kopp, Martin Holena
Evaluation of Association Rules Extracted during Anomaly Explanation. ITAT (2015)
Martin Holena, Lukás Bajer, Martin Scavnicky
Using Copulas in Data Mining Based on the Observational Calculus. IEEE Trans. Knowl. Data Eng. 27 (10) (2015)
Lukás Bajer, Martin Holena
Model Guided Sampling Optimization for Low-dimensional Problems. ICAART (2) (2015)
Lukás Bajer, Zbynek Pitra, Martin Holena
Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization. GECCO (Companion) (2015)
Zbynek Pitra, Lukás Bajer, Martin Holena
Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES. ITAT (2015)
Lukás Bajer, Zbynek Pitra, Martin Holena
Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed. GECCO (Companion) (2015)
Vojtech Kopal, Martin Holena
Comparing Non-Linear Regression Methods on Black-Box Optimization Benchmarks. ITAT (2015)
David Stefka, Martin Holena
Dynamic classifier aggregation using interaction-sensitive fuzzy measures. Fuzzy Sets Syst. 270 (2015)
Andrej Kudinov, Lukás Bajer, Zbynek Pitra, Martin Holena
Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization. ITAT (2015)
Michal Kopp, Petr Pulc, Martin Holena
Search for Structure in Audiovisual Recordings of Lectures and Conferences. ITAT (2015)
Lukás Bajer, Martin Holena
Two Gaussian Approaches to Black-Box Optomization. CoRR (2014)
Alexandre Adrien Chotard, Martin Holena
A Generalized Markov-Chain Modelling Approach to (1, λ)-ES Linear Optimization. PPSN (2014)
Alexandre Adrien Chotard, Martin Holena
A Generalized Markov-Chain Modelling Approach to (1,λ)-ES Linear Optimization: Technical Report. CoRR (2014)
Jan Gorecki, Martin Holena
Structure Determination and Estimation of Hierarchical Archimedean Copulas Based on Kendall Correlation Matrix. NFMCP (2013)
Martin Holena, Alexandre Adrien Chotard
A Generalized Markov-Chain Modelling Approach to $(1, λ)$-ES Linear Optimization. CoRR (2013)
Lukás Bajer, Viktor Charypar, Martin Holena
Model guided sampling optimization with gaussian processes for expensive black-box optimization. GECCO (Companion) (2013)
Lukás Bajer, Martin Holena
Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks. SOFSEM (2013)
Martin Holena, David Stefka
Fuzzy classification rules based on similarity. ITAT (2012)
Viktor Charypar, Martin Holena
Evolutionary optimization with active learning of surrogate models and fixed evaluation batch. ITAT (2012)
Lukás Bajer, Martin Holena
RBF-based surrogate model for evolutionary optimization. ITAT (2012)
Martin Holena, David Linke, Lukás Bajer
Surrogate modeling in the evolutionary optimization of catalytic materials. GECCO (2012)
Martin Holena, Norbert Steinfeldt, Manfred Baerns, David Stefka
Computing the correlation between catalyst composition and its performance in the catalysed process. Comput. Chem. Eng. 43 (2012)
Radim Demut, Martin Holena
Conformal sets in neural network regression. ITAT (2012)
Martin Holena, David Linke, Lukás Bajer
Case study: constraint handling in evolutionary optimization of catalytic materials. GECCO (Companion) (2011)
Martin Holena, Radim Demut
Assessing the suitability of surrogate models in evolutionary optimization. ITAT (2011)
David Stefka, Martin Holena
Dynamic Classifier Aggregation Using Fuzzy t-conorm Integral. SITIS (2011)
Lukás Bajer, Martin Holena
Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks. IDEAL (2010)
Martin Holena
Two ways of using artificial neural networks in knowledge discovery from chemical materials data. ITAT (2010)
David Stefka, Martin Holena
Dynamic classifier aggregation using fuzzy integral with interaction-sensitive fuzzy measure. ISDA (2010)
Petr Hájek, Martin Holena, Jan Rauch
The GUHA method and its meaning for data mining. J. Comput. Syst. Sci. 76 (1) (2010)
Martin Holena, David Linke, Uwe Rodemerck
Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning. SEAL (2010)
Martin Holena, David Linke, Uwe Rodemerck, Lukás Bajer
Neural Networks as Surrogate Models for Measurements in Optimization Algorithms. ASMTA (2010)
Martin Holena
Boosted Surrogate Models in Evolutionary Optimization. ITAT (2009)
David Stefka, Martin Holena
Dynamic Classifier Systems and Their Applications to Random Forest Ensembles. ICANNGA (2009)
Martin Holena
Measures of ruleset quality for general rules extraction methods. Int. J. Approx. Reason. 50 (6) (2009)
David Stefka, Martin Holena
Classifier Aggregation using Local Classification Confidence. ICAART (2009)
Martin Holena, David Linke, Norbert Steinfeldt
Boosted Neural Networks in Evolutionary Computation. ICONIP (2) (2009)
Martin Holena, Tatjana Cukic, Uwe Rodemerck, David Linke
Optimization of Catalysts Using Specific, Description-Based Genetic Algorithms. J. Chem. Inf. Model. 48 (2) (2008)
Martin Holena
Measures of quality of rulesets extracted from data. ITAT (2008)
David Stefka, Martin Holena
The Use of Fuzzy t-Conorm Integral for Combining Classifiers. ECSQARU (2007)

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Publication Activity (10 Years)

Top Topics

Top Venues

Martin Holena