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Catalyst Development for CO2 Hydrogenation to Fuels

ChemCatChem, 2013-07, Vol.5 (7), p.1948-1955

2013

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On structure, family and parameter estimation of hierarchical Archimedean copulas

Journal of statistical computation and simulation, 2017-11, Vol.87 (17), p.3261-3324

2017

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Doubly Trained Evolution Control for the Surrogate CMA-ES

Parallel Problem Solving from Nature – PPSN XIV, p.59-68

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Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis

Computers (Basel), 2023-01, Vol.12 (1), p.14

2023

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Anomaly explanation with random forests

Expert systems with applications, 2020-07, Vol.149, p.113187, Article 113187

2020

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Classification Methods for Internet Applications

1st ed. 2020., 2020

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

Catalysis today, 2003-06, Vol.81 (3), p.485-494

2003

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Gaussian Process Surrogate Models for the CMA Evolution Strategy

Evolutionary computation, 2019, Vol.27 (4), p.665-697

2019

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A dimension reduction in neural network using copula matrix

International journal of general systems, 2023-02, Vol.52 (2), p.131-146

2023

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Hierarchical Archimedean Copulas for MATLAB and Octave : The HACopula Toolbox

Journal of statistical software, 2020-05, Vol.93 (10), p.1-36

2020

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

Journal of crystal growth, 2017-08, Vol.471, p.53-61

2017

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PERFORMANCE OF CLASSIFICATION CONFIDENCE MEASURES IN DYNAMIC CLASSIFIER SYSTEMS

Neural Network World, 2013-01, Vol.23 (4), p.299-319

2013

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Dynamic classifier aggregation using interaction-sensitive fuzzy measures

Fuzzy sets and systems, 2015-07, Vol.270, p.25-52

2015

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The GUHA method and its meaning for data mining

Journal of computer and system sciences, 2010-02, Vol.76 (1), p.34-48

2010

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

Journal of intelligent information systems, 2016-02, Vol.46 (1), p.21-59

2016

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

Crystals (Basel), 2020-08, Vol.10 (8), p.663

2020

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Using Copulas in Data Mining Based on the Observational Calculus

IEEE transactions on knowledge and data engineering, 2015-10, Vol.27 (10), p.2851-2864

2015

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IEEE_Xplore

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

Catalysis science & technology, , Vol.5 (3), p.1668-1677

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Smart Design of Cz-Ge Crystal Growth Furnace and Process

Crystals (Basel), 2022-12, Vol.12 (12), p.1764

2022

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

Crystals (Basel), 2021-10, Vol.11 (10), p.1218

2021

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Identification versus generalization Comment on the criticism of indeterminacy of artificial neural networks

Applied catalysis. A, General, 2008-01, Vol.334 (1-2), p.381-385

Holena, Martin

2008

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Fast forecasting of VGF crystal growth process by dynamic neural networks

Journal of crystal growth, 2019-09, Vol.521, p.9-14

2019

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Decision Tree-Supported Analysis of Gallium Arsenide Growth Using the LEC Method

Crystals (Basel), 2023-12, Vol.13 (12), p.1659

2023

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Identification versus generalizationComment on the criticism of indeterminacy of artificial neural networks

Applied catalysis. A, General, 2008-01, Vol.334 (1-2), p.381-385

Holeňa, Martin

2008

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Identification versus generalization

Applied catalysis. A, General, 2008, Vol.334 (1), p.381-385

Holeňa, Martin

2008

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Catalyst Development for CO2 Hydrogenation to Fuels

On structure, family and parameter estimation of hierarchical Archimedean copulas

Doubly Trained Evolution Control for the Surrogate CMA-ES

Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis

Anomaly explanation with random forests

Classification Methods for Internet Applications

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

Gaussian Process Surrogate Models for the CMA Evolution Strategy

A dimension reduction in neural network using copula matrix

Hierarchical Archimedean Copulas for MATLAB and Octave : The HACopula Toolbox

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

PERFORMANCE OF CLASSIFICATION CONFIDENCE MEASURES IN DYNAMIC CLASSIFIER SYSTEMS

Dynamic classifier aggregation using interaction-sensitive fuzzy measures

The GUHA method and its meaning for data mining

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

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

Using Copulas in Data Mining Based on the Observational Calculus

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

Smart Design of Cz-Ge Crystal Growth Furnace and Process

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

Identification versus generalization Comment on the criticism of indeterminacy of artificial neural networks

Fast forecasting of VGF crystal growth process by dynamic neural networks

Decision Tree-Supported Analysis of Gallium Arsenide Growth Using the LEC Method

Identification versus generalizationComment on the criticism of indeterminacy of artificial neural networks

Identification versus generalization

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