Martin Holena

发表

Evaluation of Association Rules Extracted during Anomaly Explanation

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

The GUHA method and its meaning for data mining

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

Boosted Surrogate Models in Evolutionary Optimization

Martin Holena, M. Holeňa, 2009, ITAT.

Comparing rule mining approaches for classification with reasoning

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

Automated Selection of Covariance Function for Gaussian process Surrogate Models

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

Doubly Trained Evolution Control for the Surrogate CMA-ES

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

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

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

Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy

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

Formal logics of discovery and hypothesis formation by machine

Petr Hájek, Martin Holena, 2003, Theor. Comput. Sci..

Increasing the Diversity of Medical Data Mining through Distributed Object Technology

Martin Holena, Jana Zvárová, Anna Sochorova, 1999, MIE.

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

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

Fuzzy hypotheses for GUHA implications

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

Optimization of Catalysts Using Specific, Description-Based Genetic Algorithms

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

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

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

Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning

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

Case study: constraint handling in evolutionary optimization of catalytic materials

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

Assessment of Surrogate Model Settings Using Landscape Analysis

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

Conformal sets in neural network regression

Martin Holena, Radim Demut, 2012, ITAT.

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.

The Use of Fuzzy t-Conorm Integral for Combining Classifiers

Martin Holena, David Stefka, 2007, ECSQARU.

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

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

Fuzzy hypotheses testing in the framework of fuzzy logic

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

Testing Gaussian Process Surrogates on CEC'2013 Multi-Modal Benchmark

Martin Holena, Lukás Bajer, Nikita Orekhov, 2016, ITAT.

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

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

Boosted Neural Networks in Evolutionary Computation

Martin Holena, David Linke, Norbert Steinfeldt, 2009, ICONIP.

Computer‐Aided Design of Solid Catalysts

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

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

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

Model Guided Sampling Optimization for Low-dimensional Problems pdf

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

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

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

Loss Functions for Clustering in Multi-instance Learning

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

Extraction of Classification Rules from Sequences of Crystal Growth Data

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

Hierarchical Motion Tracking Using Matching of Sparse Features

Martin Holena, Petr Pulc, 2018, 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

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

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

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

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

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

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

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

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

Towards Landscape Analysis in Adaptive Learning of Surrogate Models

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

Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models

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

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.

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

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

Dynamic Classifier Systems and Their Applications to Random Forest Ensembles

Martin Holena, David Stefka, 2009, ICANNGA.

Measures of ruleset quality for general rules extraction methods

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

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

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

Fuzzy classification rules based on similarity

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

Genetic Algorithms for the Optimization of Catalysts in Chemical Engineering

Martin Holena, M. Holeňa, 2008 .

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

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

Wahl der Architektur eines neuronalen Netzes mittels der Theorie der Verbände

Martin Holena, Martin Holena, 1994, Fuzzy Days.

Structure Determination and Estimation of Hierarchical Archimedean Copulas Based on Kendall Correlation Matrix

Martin Holena, Jan Gorecki, 2013, NFMCP.

Image Processing in Collaborative Open Narrative Systems

Martin Holena, Petr Pulc, Erec Rosenzveig, 2016, ITAT.

Assessing the suitability of surrogate models in evolutionary optimization

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

Two ways of using artificial neural networks in knowledge discovery from chemical materials data

Martin Holena, 2010, 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.

How to Mimic Humans, Guide for Computers

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

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

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

Efficient Discovery of Nonlinear Dependencies in a Combinatorial Catalyst Data Set

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

Observational Logic Integrates Data Mining Based on Statistics and Neural Networks

Martin Holena, M. Holeňa, 2000, PKDD.

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

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

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

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

CORBA security services for health information systems

Martin Holena, Bernd Blobel, 1998, Int. J. Medical Informatics.

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

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

Online Malware Detection with Variational Autoencoders

Martin Holena, Jirí Tumpach, 2020, 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.

Two Gaussian Approaches to Black-Box Optomization pdf

Martin Holena, Lukás Bajer, 2014, ArXiv.

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.

Formal logics of discovery and hypothesis formation by machine

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

Modeling and Clustering the Behavior of Animals Using Hidden Markov Models

Martin Holena, Tomás Borovicka, Tomás Sabata, 2016, ITAT.

RBF-based surrogate model for evolutionary optimization

Martin Holena, Lukás Bajer, 2012, ITAT.

Surrogate modeling in the evolutionary optimization of catalytic materials

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

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.

Two Semi-supervised Approaches to Malware Detection with Neural Networks

Martin Holena, Marek Krcál, Jan Koza, 2020, ITAT.

Neural Networks as Surrogate Models for Measurements in Optimization Algorithms

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

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

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

Classifier Aggregation using Local Classification Confidence

Martin Holena, David Stefka, 2009, ICAART.

Using Copulas in Data Mining Based on the Observational Calculus

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

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

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

Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization

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

Measures of quality of rulesets extracted from data

Martin Holena, M. Holeňa, 2008, ITAT.

Rules Extraction from Neural Networks Trained on Multimedia Data

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

Deep Networks in Online Malware Detection

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

Measures of Ruleset Quality Capable to Represent Uncertain Validity

Martin Holena, M. Holeňa, 2007, ECSQARU.

Gaussian process surrogate models for the CMA-ES

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

Semisupervised Segmentation of UHD Video

Martin Holena, Petr Pulc, Oliver Kerul-Kmec, 2018, ITAT.

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

Martin Holena, M. Holeňa, 2005, Kybernetika.

Hierarchical Archimedean Copulas for MATLAB and Octave: The HACopula Toolbox

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

Search for Structure in Audiovisual Recordings of Lectures and Conferences

Martin Holena, Petr Pulc, Michal Kopp, 2015, ITAT.

Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks

Martin Holena, Natasha Dropka, Stefan Ecklebe, 2021 .

A Comparison of Regularization Techniques for Shallow Neural NetworksTrained on Small Datasets

Martin Holena, Jan Kalina, Jirí Tumpach, 2021, ITAT.

Combining Gaussian Processes and Neural Networks in Surrogate Modelling for Covariance Matrix Adaptation Evolution Strategy

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

Experimental Investigation of Neural and Weisfeiler-Lehman-Kernel Graph Representations for Downstream Classification

Martin Holena, Marek Dedic, Sergej Borisov, 2021, ITAT.

Video Scene Location Recognition with Neural Networks

Martin Holena, Petr Pulc, Jirí Tumpach, 2021, ITAT.

Unsupervised Construction of Task-Specific Datasets for Object Re-identification

Martin Holena, Petr Pulc, 2021, 2021 7th International Conference on Computer Technology Applications.

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

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

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 .

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

Martin Holena, David Linke, Michael Schlüter, 2015 .

New catalytic materials for the high-temperature synthesis of hydrocyanic acid from methane and ammonia by high-throughput approach

Martin Holena, Manfred Baerns, Norbert Steinfeldt, 2008 .