M. Holeňa

发表

Revealing property-performance relationships for efficient CO2 hydrogenation to higher hydrocarbons over Fe-based catalysts: Statistical analysis of literature data and its experimental validation

Qingxin Yang, A. Skrypnik, A. Matvienko, 2021 .

Deep Bayesian Semi-Supervised Active Learning for Sequence Labelling

M. Holeňa, Tomás Sabata, Juraj Eduard Páll, 2019 .

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 .

Assessing the Correlation between Descriptors of Catalytic Materials and Their Performance

M. Holeňa, N. Steinfeldt, M. Baerns, 2011 .

Knowledge-based Selection of Gaussian Process Surrogates

M. Holeňa, L. Bajer, Z. Pitra, 2019 .

Combinatorial Development of Solid Catalytic Materials: Design of High-Throughput Experiments, Data Analysis, Data Mining

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

Catalyst Development for CO2 Hydrogenation to Fuels

M. Baerns, M. Holeňa, Q. Smejkal, 2013 .

Piecewise-Linear Neural Networks and Their Relationship to Rule Extraction from Data

M. Holeňa, 2006, Neural Comput..

Loss Functions for Clustering in Multi-instance Learning

Martin Holena, Tomás Pevný, Lukás Bajer, 2020, ITAT.

Understanding trends in methane oxidation to formaldehyde: statistical analysis of literature data and based hereon experiments

M. Holeňa, E. Kondratenko, D. Linke, 2019, Catalysis Science & Technology.

Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques

M. Holeňa, N. Dropka, K. Böttcher, 2021, Crystals.

A Relational Framework for Data Mining

M. Holeňa, 2020 .

A dimension reduction in neural network using copula matrix

R. Mesiar, M. Holeňa, A. Sheikhi, 2022, Int. J. Gen. Syst..

Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context

M. Holeňa, Z. Pitra, J. Koza, 2022, ArXiv.

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

R. Kraehnert, M. Holeňa, D. Linke, 2007 .

Kendall’s tau and agglomerative clustering for structure determination of hierarchical Archimedean copulas

M. Hofert, M. Holeňa, J. Gorecki, 2017 .

Smart Design of Cz-Ge Crystal Growth Furnace and Process

M. Holeňa, N. Dropka, Xia Tang, 2022, Crystals.

Approaching the preparation of Pd-Al2O3 catalyst systematically: one step closer to reproducible catalyst synthesis

M. Holeňa, D. Linke, U. Dingerdissen, 2006 .

Background of Combinatorial Catalyst Development

M. Holeňa, M. Baerns, 2009 .

Analysis and Mining of Data Collected in Catalytic Experiments

M. Holeňa, M. Baerns, 2009 .

Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks

Martin Holena, Lukás Bajer, M. Holeňa, 2013, SOFSEM.

Important Internet Applications of Classification

Martin Kopp, Martin Holeňa, Petr Pulc, 2020 .

Basic Concepts Concerning Classification

Martin Kopp, Martin Holeňa, Petr Pulc, 2020 .

Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials

M. Baerns, M. Holena, M. Baerns, 2004 .

Deep Networks in Online Malware Detection

Martin Holena, Marek Krcál, Jirí Tumpach, 2019, ITAT.

Piecewise-Linear Neural Networks and Their Relationship to Rule Extraction from Data

Martin Holena, M. Holeňa, 2006, Neural Comput..

Some Frequently Used Classification Methods

Martin Kopp, Martin Holeňa, Petr Pulc, 2020 .

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

Martin Holeňa, David Linke, Uwe Rodemerck, 2011 .

Statistical Analysis of Past Catalytic Data on Oxidative Methane Coupling for New Insights into the Composition of High‐Performance Catalysts

Martin Holena, Robert Schlögl, R. Schlögl, 2011 .

Evolutionary Dynamic Optimization of Control Trajectories for the Catalytic Transformation of the Bioethanol-To-Olefins Process using Neural Networks

Gorka Sorrosal, Cruz E. Borges, Ainhoa Alonso-Vicario, 2016, GECCO.

Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks

Martin Holena, Lukás Bajer, M. Holeňa, 2010, IDEAL.

Extraction of Classification Rules from Sequences of Crystal Growth Data

Martin Holena, Yann Dauxais, Natasha Dropka, 2020, ITAT.

Semi-supervised and Active Learning in Video Scene Classification from Statistical Features

Martin Holena, Petr Pulc, Tomás Sabata, 2018, IAL@PKDD/ECML.

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

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

Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization

Martin Holena, Jakub Repický, Zbynek Pitra, 2018, IAL@PKDD/ECML.

Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy

Martin Holena, Jakub Repický, Zbynek Pitra, 2019, GECCO.

Surrogate modeling in the evolutionary optimization of catalytic materials

Martin Holena, David Linke, Lukás Bajer, 2012, GECCO '12.

Comparing middleware concepts for advanced healthcare system architectures.

B Blobel, M Holena, B. Blobel, 1997, International journal of medical informatics.

Aiming at Comprehensibility

Martin Kopp, Martin Holeňa, Petr Pulc, 2020 .

Using Copulas in Data Mining Based on the Observational Calculus

Martin Holena, Lukás Bajer, Martin Scavnicky, 2015, IEEE Transactions on Knowledge and Data Engineering.

Measures of ruleset quality for general rules extraction methods

Martin Holena, M. Holeňa, 2009, Int. J. Approx. Reason..

Measures of quality of rulesets extracted from data

Martin Holena, M. Holeňa, 2008, ITAT.

Measures of Ruleset Quality Capable to Represent Uncertain Validity

Martin Holena, M. Holeňa, 2007, ECSQARU.

Extraction of fuzzy logic rules from data by means of artificial neural networks

Martin Holena, M. Holeňa, 2005, Kybernetika.

Fuzzy hypotheses testing in the framework of fuzzy logic

Martin Holena, M. Holeňa, 2004, Fuzzy Sets Syst..

Extraction of Logical Rules from Data by Means of Piecewise-Linear Neural Networks

Martin Holena, M. Holeňa, 2002, Discovery Science.

Observational Logic Integrates Data Mining Based on Statistics and Neural Networks

Martin Holena, M. Holeňa, 2000, PKDD.

Fuzzy hypotheses for GUHA implications

Martin Holena, M. Holeňa, 1998, Fuzzy Sets Syst..

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

Martin Holeňa, David Linke, Uwe Rodemerck, 2006 .

Using neural networks to tune heuristic parameters in evolutionary optimization

Martin Holeňa, M. Holeňa, 2006 .

How to Mimic Humans, Guide for Computers

Martin Kopp, Martin Holena, Matous Pistora, 2016, ITAT.

A Generalized Markov-Chain Modelling Approach to (1, λ)-ES Linear Optimization pdf

Martin Holena, Alexandre Adrien Chotard, A. Chotard, 2013, PPSN.

A Generalized Markov-Chain Modelling Approach to $(1,\lambda )$-ES Linear Optimization: Technical Report

Martin Holena, Alexandre Adrien Chotard, A. Chotard, 2013 .

Formal logics of discovery and hypothesis formation by machine

Petr Hájek, Martin Holena, P. Hájek, 1998, Theor. Comput. Sci..

Computing the correlation between catalyst composition and its performance in the catalysed process

Martin Holena, Manfred Baerns, David Stefka, 2012, Comput. Chem. Eng..

Developing catalytic materials for the oxidative coupling of methane through statistical analysis of literature data

Martin Holena, David Linke, Michael Schlüter, 2015 .

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

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

Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts

N. Kockmann, M. Holeňa, Alexander S. Behr, 2022, ITAT.

The GUHA Method and Foundations of (Relational) Data Mining

Petr Hájek, Martin Holena, Jan Rauch, 2003, Theory and Applications of Relational Structures as Knowledge Instruments.

Doubly Trained Evolution Control for the Surrogate CMA-ES

Martin Holena, Zbynek Pitra, Lukás Bajer, 2016, PPSN.

Integrating Frames, Rules and Uncertainty in a Database-Coupled Knowledge-Representation System

Martin Holena, Gernod Laufkötter, Rainer Kruschinski, 1994, DEXA.

The GUHA method and its meaning for data mining

Petr Hájek, Martin Holena, Jan Rauch, 2010, J. Comput. Syst. Sci..

Efficient Discovery of Nonlinear Dependencies in a Combinatorial Catalyst Data Set

Martin Holena, Manfred Baerns, James N. Cawse, 2004, J. Chem. Inf. Model..

Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning

Martin Holena, David Linke, Uwe Rodemerck, 2010, SEAL.

Evolutionary computation system for problem-tailored genetic optimization of catalytic materials

Martin Holena, David Linke, Uwe Rodemerck, 2010, 2010 IEEE 9th International Conference on Cyberntic Intelligent Systems.

Tuning Evolutionary Algorithms with Artificial Neural Networks

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

Mathematical Methods of Searching for Optimal Catalytic Materials

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

Computer‐Aided Design of Solid Catalysts

Martin Holena, Manfred Baerns, M. Holeňa, 2008 .

Optimization of Catalysts Using Specific, Description-Based Genetic Algorithms

Martin Holena, David Linke, Uwe Rodemerck, 2008, J. Chem. Inf. Model..

Applications of Combinatorial Catalyst Development and an Outlook on Future Work

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

Boosted Surrogate Models in Evolutionary Optimization

Martin Holena, M. Holeňa, 2009, ITAT.

Genetic Algorithms for the Optimization of Catalysts in Chemical Engineering

Martin Holena, M. Holeňa, 2008 .

Case study: constraint handling in evolutionary optimization of catalytic materials

Martin Holena, David Linke, Lukás Bajer, 2011, GECCO.

Assessing the suitability of surrogate models in evolutionary optimization

Martin Holena, Radim Demut, M. Holeňa, 2011, ITAT.

Comparing rule mining approaches for classification with reasoning

Martin Kopp, Martin Holena, Marek Jílek, 2018, ITAT.

Application of Artiﬁcial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

Natasha Dropka, Martin Holena, M. Holeňa, 2022 .

Improving Neural Network Approximations

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

Interpreting and clustering outliers with sapling random forests

Martin Holeňa, Tomáš Pevný, Martin Kopp, 2014 .

Fast forecasting of VGF crystal growth process by dynamic neural networks

Jan Winkler, Natasha Dropka, Martin Holena, 2019, Journal of Crystal Growth.

Optimization of magnetically driven directional solidification of silicon using artificial neural networks and Gaussian process models

Martin Holena, Natasha Dropka, M. Holeňa, 2017 .

Interaction between model and its evolution control in surrogate-assisted CMA evolution strategy

Martin Holena, Zbynek Pitra, Jirí Tumpach, 2021, GECCO.

Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models

Martin Holena, Jakub Repický, Zbynek Pitra, 2017, ITAT.

Overview of surrogate-model versions of covariance matrix adaptation evolution strategy

Martin Holena, Jakub Repický, Zbynek Pitra, 2017, GECCO.

Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy

Martin Holena, Jakub Repický, Zbynek Pitra, 2017, GECCO.

Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed

Martin Holena, Zbynek Pitra, Lukás Bajer, 2015, GECCO.

Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization

Martin Holena, Zbynek Pitra, Lukás Bajer, 2015, GECCO.

Model Guided Sampling Optimization for Low-dimensional Problems pdf

Martin Holena, Lukás Bajer, M. Holeňa, 2015, ICAART.

Model guided sampling optimization with gaussian processes for expensive black-box optimization

Martin Holena, Lukás Bajer, Viktor Charypar, 2013, GECCO.

Assessment of Surrogate Model Settings Using Landscape Analysis

Martin Holena, Zbynek Pitra, Mikulás Dvorák, 2020, ITAT.

Towards Landscape Analysis in Adaptive Learning of Surrogate Models

Martin Holena, Zbynek Pitra, M. Holeňa, 2020, IAL@PKDD/ECML.

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

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

Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization

Martin Holena, Jakub Repický, Zbynek Pitra, 2018, IAL@PKDD/ECML.

Automated Selection of Covariance Function for Gaussian process Surrogate Models

Martin Holena, Jakub Repický, Zbynek Pitra, 2018, ITAT.

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

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

Traditional Gaussian Process Surrogates in the BBOB Framework

Martin Holena, Jakub Repický, Lukás Bajer, 2016, ITAT.

Comparing Non-Linear Regression Methods on Black-Box Optimization Benchmarks

Martin Holena, Vojtech Kopal, M. Holeňa, 2015, ITAT.

Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES

Martin Holena, Zbynek Pitra, Lukás Bajer, 2015, ITAT.

On structure, family and parameter estimation of hierarchical Archimedean copulas

Martin Holeňa, Marius Hofert, Jan Górecki, 2016, 1611.09225.

Dynamic classifier aggregation using interaction-sensitive fuzzy measures

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

Dynamic Classifier Aggregation Using Fuzzy t-conorm Integral

Martin Holena, David Stefka, M. Holeňa, 2011, 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems.

Dynamic classifier aggregation using fuzzy integral with interaction-sensitive fuzzy measure

Martin Holena, David Stefka, M. Holeňa, 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

Performance of classifcation confdence measures in dynamic classifer systems

David Štefka, Martin Holeňa, M. Holeňa, 2013 .

Fuzzy classification rules based on similarity

Martin Holena, David Stefka, M. Holeňa, 2012, ITAT.

Evolutionary optimization with active learning of surrogate models and fixed evaluation batch

Martin Holena, Viktor Charypar, M. Holeňa, 2012, ITAT.

Hierarchical Archimedean Copulas for MATLAB and Octave: The HACopula Toolbox

Martin Holena, Marius Hofert, Jan Górecki, 2020, Journal of Statistical Software.

Evaluation of Association Rules Extracted during Anomaly Explanation

Martin Kopp, Martin Holena, M. Holeňa, 2015, ITAT.

Balancing performance and complexity with adaptive graph coarsening

M. Holeňa, P. Procházka, L. Bajer, 2023 .

An approach to structure determination and estimation of hierarchical Archimedean Copulas and its application to Bayesian classification

Martin Holena, Marius Hofert, Jan Gorecki, 2016, Journal of Intelligent Information Systems.

Rules Extraction from Neural Networks Trained on Multimedia Data

Martin Holena, Petr Pulc, Matej Fanta, 2019, ITAT.

Gaussian Process Surrogate Models for the CMA Evolution Strategy

M. Holeňa, L. Bajer, Z. Pitra, 2019, Evolutionary Computation.

Improving Optimization with Gaussian Processes in the Covariance Matrix Adaptation Evolution Strategy

M. Holeňa, J. Koza, Jirí Tumpach, 2023, ITAT.