Integrating Frames, Rules and Uncertainty in a Database-Coupled Knowledge-Representation System. - Petra Drescher; Martin Holena; Rainer Kruschinski; Gernod Laufkötter (DEXA, 1994)

Wahl der Architektur eines neuronalen Netzes mittels der Theorie der Verbände. - Martin Holena (Fuzzy Days, 1994)

Fuzzy hypotheses for GUHA implications. - Martin Holena (Fuzzy Sets Syst., 1998)

CORBA security services for health information systems. - Bernd Blobel; Martin Holena (Int. J. Medical Informatics, 1998)

Increasing the Diversity of Medical Data Mining through Distributed Object Technology. - Martin Holena; Anna Sochorova; Jana Zvárová (MIE, 1999)

Observational Logic Integrates Data Mining Based on Statistics and Neural Networks. - Martin Holena (PKDD, 2000)

Extraction of Logical Rules from Data by Means of Piecewise-Linear Neural Networks. - Martin Holena (Discovery Science, 2002)

The GUHA Method and Foundations of (Relational) Data Mining. - Petr Hájek 001; Martin Holena; Jan Rauch (Theory and Applications of Relational Structures as Knowledge Instruments, 2003)

Formal logics of discovery and hypothesis formation by machine. - Petr Hájek 001; Martin Holena (Theor. Comput. Sci., 2003)

Fuzzy hypotheses testing in the framework of fuzzy logic. - Martin Holena (Fuzzy Sets Syst., 2004)

Efficient Discovery of Nonlinear Dependencies in a Combinatorial Catalyst Data Set. - James N. Cawse; Manfred Baerns; Martin Holena (J. Chem. Inf. Model., 2004)

Extraction of fuzzy logic rules from data by means of artificial neural networks. - Martin Holena (Kybernetika, 2005)

Piecewise-Linear Neural Networks and Their Relationship to Rule Extraction from Data. - Martin Holena (Neural Comput., 2006)

Measures of Ruleset Quality Capable to Represent Uncertain Validity. - Martin Holena (ECSQARU, 2007)

The Use of Fuzzy t-Conorm Integral for Combining Classifiers. - David Stefka; Martin Holena (ECSQARU, 2007)

Measures of quality of rulesets extracted from data. - Martin Holena (ITAT, 2008)

Optimization of Catalysts Using Specific, Description-Based Genetic Algorithms. - Martin Holena; Tatjana Cukic; Uwe Rodemerck; David Linke (J. Chem. Inf. Model., 2008)

Classifier Aggregation using Local Classification Confidence. - David Stefka; Martin Holena (ICAART, 2009)

Dynamic Classifier Systems and Their Applications to Random Forest Ensembles. - David Stefka; Martin Holena (ICANNGA, 2009)

Boosted Neural Networks in Evolutionary Computation. - Martin Holena; David Linke; Norbert Steinfeldt (ICONIP (2), 2009)

Boosted Surrogate Models in Evolutionary Optimization. - Martin Holena (ITAT, 2009)

Measures of ruleset quality for general rules extraction methods. - Martin Holena (Int. J. Approx. Reason., 2009)

Neural Networks as Surrogate Models for Measurements in Optimization Algorithms. - Martin Holena; David Linke; Uwe Rodemerck; Lukás Bajer (ASMTA, 2010)

Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks. - Lukás Bajer; Martin Holena (IDEAL, 2010)

Dynamic classifier aggregation using fuzzy integral with interaction-sensitive fuzzy measure. - David Stefka; Martin Holena (ISDA, 2010)

Two ways of using artificial neural networks in knowledge discovery from chemical materials data. - Martin Holena (ITAT, 2010)

Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning. - Martin Holena; David Linke; Uwe Rodemerck (SEAL, 2010)

The GUHA method and its meaning for data mining. - Petr Hájek 001; Martin Holena; Jan Rauch (J. Comput. Syst. Sci., 2010)

Case study: constraint handling in evolutionary optimization of catalytic materials. - Martin Holena; David Linke; Lukás Bajer (GECCO (Companion), 2011)

Assessing the suitability of surrogate models in evolutionary optimization. - Martin Holena; Radim Demut (ITAT, 2011)

Dynamic Classifier Aggregation Using Fuzzy t-conorm Integral. - David Stefka; Martin Holena (SITIS, 2011)

Surrogate modeling in the evolutionary optimization of catalytic materials. - Martin Holena; David Linke; Lukás Bajer (GECCO, 2012)

RBF-based surrogate model for evolutionary optimization. - Lukás Bajer; Martin Holena (ITAT, 2012)

Evolutionary optimization with active learning of surrogate models and fixed evaluation batch. - Viktor Charypar; Martin Holena (ITAT, 2012)

Conformal sets in neural network regression. - Radim Demut; Martin Holena (ITAT, 2012)

Fuzzy classification rules based on similarity. - Martin Holena; David Stefka (ITAT, 2012)

Computing the correlation between catalyst composition and its performance in the catalysed process. - Martin Holena; Norbert Steinfeldt; Manfred Baerns; David Stefka (Comput. Chem. Eng., 2012)

Model guided sampling optimization with gaussian processes for expensive black-box optimization. - Lukás Bajer; Viktor Charypar; Martin Holena (GECCO (Companion), 2013)

Structure Determination and Estimation of Hierarchical Archimedean Copulas Based on Kendall Correlation Matrix. - Jan Górecki; Martin Holena (NFMCP, 2013)

Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks. - Lukás Bajer; Martin Holena (SOFSEM, 2013)

A Generalized Markov-Chain Modelling Approach to (1, λ)-ES Linear Optimization. - Alexandre Adrien Chotard; Martin Holena (PPSN, 2014)

Two Gaussian Approaches to Black-Box Optomization. - Lukás Bajer; Martin Holena (CoRR, 2014)

A Generalized Markov-Chain Modelling Approach to (1,λ)-ES Linear Optimization: Technical Report. - Alexandre Adrien Chotard; Martin Holena (CoRR, 2014)

Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed. - Lukás Bajer; Zbynek Pitra; Martin Holena (GECCO (Companion), 2015)

Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization. - Lukás Bajer; Zbynek Pitra; Martin Holena (GECCO (Companion), 2015)

Model Guided Sampling Optimization for Low-dimensional Problems. - Lukás Bajer; Martin Holena (ICAART (2), 2015)

Comparing Non-Linear Regression Methods on Black-Box Optimization Benchmarks. - Vojtech Kopal; Martin Holena (ITAT, 2015)

Evaluation of Association Rules Extracted during Anomaly Explanation. - Martin Kopp; Martin Holena (ITAT, 2015)

Search for Structure in Audiovisual Recordings of Lectures and Conferences. - Michal Kopp; Petr Pulc; Martin Holena (ITAT, 2015)

Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization. - Andrej Kudinov; Lukás Bajer; Zbynek Pitra; Martin Holena (ITAT, 2015)

Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES. - Zbynek Pitra; Lukás Bajer; Martin Holena (ITAT, 2015)

Dynamic classifier aggregation using interaction-sensitive fuzzy measures. - David Stefka; Martin Holena (Fuzzy Sets Syst., 2015)

Using Copulas in Data Mining Based on the Observational Calculus. - Martin Holena; Lukás Bajer; Martin Scavnicky (IEEE Trans. Knowl. Data Eng., 2015)

Evolutionary Dynamic Optimization of Control Trajectories for the Catalytic Transformation of the Bioethanol-To-Olefins Process using Neural Networks. - Gorka Sorrosal; Cruz E. Borges; Martin Holeña; Ana María Macarulla; Cristina Martin Andonegui; Ainhoa Alonso-Vicario (GECCO (Companion), 2016)

How to Mimic Humans, Guide for Computers. - Martin Kopp; Matous Pistora; Martin Holena (ITAT, 2016)

Testing Gaussian Process Surrogates on CEC'2013 Multi-Modal Benchmark. - Nikita Orekhov; Lukás Bajer; Martin Holena (ITAT, 2016)

Image Processing in Collaborative Open Narrative Systems. - Petr Pulc; Erec Rosenzveig; Martin Holena (ITAT, 2016)

Traditional Gaussian Process Surrogates in the BBOB Framework. - Jakub Repický; Lukás Bajer; Martin Holena (ITAT, 2016)

Modeling and Clustering the Behavior of Animals Using Hidden Markov Models. - Tomás Sabata; Tomás Borovicka; Martin Holena (ITAT, 2016)

Doubly Trained Evolution Control for the Surrogate CMA-ES. - Zbynek Pitra; Lukás Bajer; Martin Holena (PPSN, 2016)

An approach to structure determination and estimation of hierarchical Archimedean Copulas and its application to Bayesian classification. - Jan Górecki; Marius Hofert; Martin Holena (J. Intell. Inf. Syst., 2016)

Towards Real-time Motion Estimation in High-Definition Video Based on Points of Interest. - Petr Pulc; Martin Holeña (FedCSIS, 2017)

Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy. - Zbynek Pitra; Lukás Bajer; Jakub Repický; Martin Holena (GECCO (Companion), 2017)

Overview of surrogate-model versions of covariance matrix adaptation evolution strategy. - Zbynek Pitra; Lukás Bajer; Jakub Repický; Martin Holena (GECCO (Companion), 2017)

Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy. - Zbynek Pitra; Lukás Bajer; Jakub Repický; Martin Holena (GECCO (Companion), 2017)

Breaking CAPTCHAs with Convolutional Neural Networks. - Martin Kopp; Matej Nikl; Martin Holena (ITAT, 2017)

Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy. - Zbynek Pitra; Lukás Bajer; Jakub Repický; Martin Holena (ITAT, 2017)

Random-Forest-Based Analysis of URL Paths. - Jakub Puchýr; Martin Holena (ITAT, 2017)

Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models. - Jakub Repický; Lukás Bajer; Zbynek Pitra; Martin Holena (ITAT, 2017)

K-best Viterbi Semi-supervized Active Learning in Sequence Labelling. - Tomás Sabata; Tomás Borovicka; Martin Holena (ITAT, 2017)

Semisupervised Segmentation of UHD Video. - Oliver Kerul-Kmec; Petr Pulc; Martin Holena (ITAT, 2018)

Comparing rule mining approaches for classification with reasoning. - Martin Kopp; Marek Jílek; Martin Holena (ITAT, 2018)

Sentiment Analysis from Utterances. - Jiri Kozusznik; Petr Pulc; Martin Holena (ITAT, 2018)

Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy. - Zbynek Pitra; Jakub Repický; Martin Holena (ITAT, 2018)

Automated Selection of Covariance Function for Gaussian process Surrogate Models. - Jakub Repický; Martin Holena; Zbynek Pitra (ITAT, 2018)

Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization. - Zbynek Pitra; Jakub Repický; Martin Holena (IAL@PKDD/ECML, 2018)

Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization. - Jakub Repický; Zbynek Pitra; Martin Holena (IAL@PKDD/ECML, 2018)

Semi-supervised and Active Learning in Video Scene Classification from Statistical Features. - Tomás Sabata; Petr Pulc; Martin Holena (IAL@PKDD/ECML, 2018)

Hierarchical Motion Tracking Using Matching of Sparse Features. - Petr Pulc; Martin Holena (SITIS, 2018)

Gaussian process surrogate models for the CMA-ES. - Lukás Bajer; Zbynek Pitra; Jakub Repický; Martin Holena (GECCO (Companion), 2019)

Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy. - Zbynek Pitra; Jakub Repický; Martin Holena (GECCO, 2019)

Rules Extraction from Neural Networks Trained on Multimedia Data. - Matej Fanta; Petr Pulc; Martin Holena (ITAT, 2019)

Deep Networks in Online Malware Detection. - Jirí Tumpach; Marek Krcál; Martin Holena (ITAT, 2019)

Gaussian Process Surrogate Models for the CMA Evolution Strategy. - Lukás Bajer; Zbynek Pitra; Jakub Repický; Martin Holena (Evol. Comput., 2019)

Extraction of Classification Rules from Sequences of Crystal Growth Data. - Radek Busa; Yann Dauxais; Stefan Ecklebe; Natasha Dropka; Martin Holena (ITAT, 2020)

Loss Functions for Clustering in Multi-instance Learning. - Marek Dedic; Tomás Pevný; Lukás Bajer; Martin Holena (ITAT, 2020)

Assessment of Surrogate Model Settings Using Landscape Analysis. - Mikulás Dvorák; Zbynek Pitra; Martin Holena (ITAT, 2020)

Two Semi-supervised Approaches to Malware Detection with Neural Networks. - Jan Koza; Marek Krcál; Martin Holena (ITAT, 2020)

Online Malware Detection with Variational Autoencoders. - Jirí Tumpach; Martin Holena (ITAT, 2020)

Towards Landscape Analysis in Adaptive Learning of Surrogate Models. - Zbynek Pitra; Martin Holena (IAL@PKDD/ECML, 2020)

Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings. - Tomás Sabata; Martin Holena (IAL@PKDD/ECML, 2020)

Anomaly explanation with random forests. - Martin Kopp; Tomás Pevný; Martin Holena (Expert Syst. Appl., 2020)

Interaction between model and its evolution control in surrogate-assisted CMA evolution strategy. - Zbynek Pitra; Marek Hanus; Jan Koza; Jirí Tumpach; Martin Holena (GECCO, 2021)

Unsupervised Construction of Task-Specific Datasets for Object Re-identification. - Petr Pulc; Martin Holena (ICCTA, 2021)

Experimental Investigation of Neural and Weisfeiler-Lehman-Kernel Graph Representations for Downstream Classification. - Sergej Borisov; Marek Dedic; Martin Holena (ITAT, 2021)

Video Scene Location Recognition with Neural Networks. - Lukás Korel; Petr Pulc; Jirí Tumpach; Martin Holena (ITAT, 2021)

Combining Gaussian Processes and Neural Networks in Surrogate Modelling for Covariance Matrix Adaptation Evolution Strategy. - Jan Koza; Jirí Tumpach; Zbynek Pitra; Martin Holena (ITAT, 2021)

A Comparison of Regularization Techniques for Shallow Neural NetworksTrained on Small Datasets. - Jirí Tumpach; Jan Kalina; Martin Holena (ITAT, 2021)

Using Past Experience for Configuration of Gaussian Processes in Black-Box Optimization. - Jan Koza; Jirí Tumpach; Zbynek Pitra; Martin Holena (LION, 2021)

Combining Gaussian Processes with Neural Networks for Active Learning in Optimization. - Jirí Ruzicka; Jan Koza; Jirí Tumpach; Zbynek Pitra; Martin Holena (IAL@PKDD/ECML, 2021)

Neural-network-based estimation of normal distributions in black-box optimization. - Jirí Tumpach; Jan Koza; Martin Holena (ESANN, 2022)

On Combining Robustness and Regularization in Training Multilayer Perceptrons over Small Data. - Jan Kalina; Jirí Tumpach; Martin Holena (IJCNN, 2022)

Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts. - Lukás Korel; Alexander S. Behr; Norbert Kockmann; Martin Holena (ITAT, 2022)

Text-to-Ontology Mapping via Natural Language Processing Models. - Uladzislau Yorsh; Alexander S. Behr; Norbert Kockmann; Martin Holena (ITAT, 2022)

Using Paraphrasers to Detect Duplicities in Ontologies. - Lukás Korel; Alexander S. Behr; Norbert Kockmann; Martin Holena (KEOD, 2023)

Improving Optimization with Gaussian Processes in the Covariance Matrix Adaptation Evolution Strategy. - Jirí Tumpach; Jan Koza; Martin Holena (ITAT, 2023)

Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis. - Lukás Korel; Uladzislau Yorsh; Alexander S. Behr; Norbert Kockmann; Martin Holena (Comput., 2023)

A dimension reduction in neural network using copula matrix. - Ayyub Sheikhi; Radko Mesiar; Martin Holena (Int. J. Gen. Syst., 2023)

Suitability of Modern Neural Networks for Active and Transfer Learning in Surrogate-Assisted Black-Box Optimization. - Martin Holena; Jan Koza (IAL@PKDD/ECML, 2024)

Balancing performance and complexity with adaptive graph coarsening. - Marek Dedic; Lukás Bajer; Pavel Procházka; Martin Holena (Tiny Papers @ ICLR, 2024)

On Difficulties of Attention Factorization through Shared Memory. - Uladzislau Yorsh; Martin Holena; Ondrej Bojar; David Herel (Tiny Papers @ ICLR, 2024)

Graph Neural Networks and Deep Reinforcement Learning in Job Scheduling. - Maros Bratko; Thomas Seidelmann; Martin Holena (ITAT, 2024)

Textual Embeddings with Word-Type-Weighted Word2Vec. - Theodor Ladin; Lukás Korel; Martin Holena (ITAT, 2024)

Response prediction of antidepressants: Using graph theory tools for brain network connectivity analysis. - Akbar Davoodi; Martin Holena; Martin Brunovsky; Aditi Kathpalia; Jaroslav Hlinka; Martin Bares; Milan Palus (Biomed. Signal Process. Control., 2025)

Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context. - Zbynek Pitra; Jan Koza; Jirí Tumpach; Martin Holena (Evol. Comput., 2025)