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2020 – today
- 2025
[j19]Akbar Davoodi, Martin Holena, Martin Brunovsky, Aditi Kathpalia, Jaroslav Hlinka, Martin Bares, Milan Palus:
Response prediction of antidepressants: Using graph theory tools for brain network connectivity analysis. Biomed. Signal Process. Control. 103: 107362 (2025)- 2024
- [c93]Martin Holena, Jan Koza:
Suitability of Modern Neural Networks for Active and Transfer Learning in Surrogate-Assisted Black-Box Optimization. IAL@PKDD/ECML 2024: 47-67 - [c92]Marek Dedic
, Lukás Bajer, Pavel Procházka, Martin Holena:
Balancing performance and complexity with adaptive graph coarsening. Tiny Papers @ ICLR 2024 - [c91]Uladzislau Yorsh, Martin Holena, Ondrej Bojar, David Herel:
On Difficulties of Attention Factorization through Shared Memory. Tiny Papers @ ICLR 2024 - [c90]Maros Bratko, Thomas Seidelmann, Martin Holena:
Graph Neural Networks and Deep Reinforcement Learning in Job Scheduling. ITAT 2024: 15-23 - [c89]Theodor Ladin, Lukás Korel, Martin Holena:
Textual Embeddings with Word-Type-Weighted Word2Vec. ITAT 2024: 37-42 - [e6]Lucie Ciencialová, Martin Holena, Róbert Jajcay, Tatiana Jajcayová, Martin Macaj, Frantisek Mráz, Richard Ostertág, Dana Pardubská, Martin Plátek, Martin Stanek:
Proceedings of the 24th Conference Information Technologies - Applications and Theory (ITAT 2024), Drienica, Slovakia, September 20-24, 2024. CEUR Workshop Proceedings 3792, CEUR-WS.org 2024 [contents] - [i9]Uladzislau Yorsh, Martin Holeña, Ondrej Bojar, David Herel:
On Difficulties of Attention Factorization through Shared Memory. CoRR abs/2404.00798 (2024) - 2023
- [j18]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): 14 (2023) - [j17]Ayyub Sheikhi, Radko Mesiar, Martin Holena:
A dimension reduction in neural network using copula matrix. Int. J. Gen. Syst. 52(2): 131-146 (2023) - [c88]Lukás Korel, Alexander S. Behr, Norbert Kockmann, Martin Holena:
Using Paraphrasers to Detect Duplicities in Ontologies. KEOD 2023: 40-49 - [c87]Jirí Tumpach, Jan Koza, Martin Holena:
Improving Optimization with Gaussian Processes in the Covariance Matrix Adaptation Evolution Strategy. ITAT 2023: 82-88 - [e5]Brona Brejová, Lucie Ciencialová, Martin Holena, Róbert Jajcay, Tatiana Jajcayová, Matej Lexa, Frantisek Mráz, Dana Pardubská, Martin Plátek:
Proceedings of the 23rd Conference Information Technologies - Applications and Theory (ITAT 2023), Tatranské Matliare, Slovakia, September 22-26, 2023. CEUR Workshop Proceedings 3498, CEUR-WS.org 2023 [contents] - [i8]Lukás Korel, Alexander S. Behr, Norbert Kockmann, Martin Holena:
Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts. CoRR abs/2309.09203 (2023) - [i7]Lukás Korel, Petr Pulc, Jirí Tumpach, Martin Holena:
Video Scene Location Recognition with Neural Networks. CoRR abs/2309.11928 (2023) - 2022
- [c86]Jirí Tumpach, Jan Koza, Martin Holena:
Neural-network-based estimation of normal distributions in black-box optimization. ESANN 2022 - [c85]Jan Kalina, Jirí Tumpach, Martin Holena:
On Combining Robustness and Regularization in Training Multilayer Perceptrons over Small Data. IJCNN 2022: 1-8 - [c84]Uladzislau Yorsh, Alexander S. Behr, Norbert Kockmann, Martin Holena:
Text-to-Ontology Mapping via Natural Language Processing Models. ITAT 2022: 28-34 - [c83]Lukás Korel, Alexander S. Behr, Norbert Kockmann, Martin Holena:
Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts. ITAT 2022: 44-54 - [e4]Lucie Ciencialová, Martin Holena, Robert Jajcay, Tatiana Jajcayová, Frantisek Mráz, Dana Pardubská, Martin Plátek:
Proceedings of the 22nd Conference Information Technologies - Applications and Theory (ITAT 2022), Zuberec, Slovakia, September 23-27, 2022. CEUR Workshop Proceedings 3226, CEUR-WS.org 2022 [contents] - [i6]Zbynek Pitra, Jan Koza, Jirí Tumpach, Martin Holena:
Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context. CoRR abs/2203.11315 (2022) - 2021
- [c82]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: 528-536 - [c81]Petr Pulc, Martin Holena:
Unsupervised Construction of Task-Specific Datasets for Object Re-identification. ICCTA 2021: 66-72 - [c80]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: 29-38 - [c79]Lukás Korel, Petr Pulc, Jirí Tumpach, Martin Holena:
Video Scene Location Recognition with Neural Networks. ITAT 2021: 85-93 - [c78]Jirí Tumpach, Jan Kalina, Martin Holena:
A Comparison of Regularization Techniques for Shallow Neural NetworksTrained on Small Datasets. ITAT 2021: 94-103 - [c77]Sergej Borisov, Marek Dedic, Martin Holena:
Experimental Investigation of Neural and Weisfeiler-Lehman-Kernel Graph Representations for Downstream Classification. ITAT 2021: 130-139 - [c76]Jan Koza, Jirí Tumpach, Zbynek Pitra, Martin Holena:
Using Past Experience for Configuration of Gaussian Processes in Black-Box Optimization. LION 2021: 167-182 - [c75]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: 105-120 - [e3]Brona Brejová, Lucie Ciencialová, Martin Holena, Frantisek Mráz, Dana Pardubská, Martin Plátek, Tomás Vinar:
Proceedings of the 21st Conference Information Technologies - Applications and Theory (ITAT 2021), Hotel Heľpa, Nízke Tatry and Muránska planina, Slovakia, September 24-28, 2021. CEUR Workshop Proceedings 2962, CEUR-WS.org 2021 [contents] - 2020
- [j16]Martin Kopp, Tomás Pevný, Martin Holena:
Anomaly explanation with random forests. Expert Syst. Appl. 149: 113187 (2020) - [c74]Mikulás Dvorák, Zbynek Pitra, Martin Holena:
Assessment of Surrogate Model Settings Using Landscape Analysis. ITAT 2020: 81-89 - [c73]Radek Busa, Yann Dauxais, Stefan Ecklebe, Natasha Dropka, Martin Holena:
Extraction of Classification Rules from Sequences of Crystal Growth Data. ITAT 2020: 101-107 - [c72]Jirí Tumpach, Martin Holena:
Online Malware Detection with Variational Autoencoders. ITAT 2020: 122-129 - [c71]Marek Dedic, Tomás Pevný, Lukás Bajer, Martin Holena:
Loss Functions for Clustering in Multi-instance Learning. ITAT 2020: 137-146 - [c70]Jan Koza, Marek Krcál, Martin Holena:
Two Semi-supervised Approaches to Malware Detection with Neural Networks. ITAT 2020: 176-185 - [c69]Tomás Sabata, Martin Holena:
Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings. IAL@PKDD/ECML 2020: 72-77 - [c68]Zbynek Pitra, Martin Holena:
Towards Landscape Analysis in Adaptive Learning of Surrogate Models. IAL@PKDD/ECML 2020: 78-83 - [e2]Martin Holena, Tomás Horváth, Alica Kelemenová, Frantisek Mráz, Dana Pardubská, Martin Plátek, Petr Sosík:
Proceedings of the 20th Conference Information Technologies - Applications and Theory (ITAT 2020), Hotel Tyrapol, Oravská Lesná, Slovakia, September 18-22, 2020. CEUR Workshop Proceedings 2718, CEUR-WS.org 2020 [contents]
2010 – 2019
- 2019
- [j15]Lukás Bajer, Zbynek Pitra, Jakub Repický, Martin Holena:
Gaussian Process Surrogate Models for the CMA Evolution Strategy. Evol. Comput. 27(4): 665-697 (2019) - [c67]Lukás Bajer, Zbynek Pitra, Jakub Repický, Martin Holena:
Gaussian process surrogate models for the CMA-ES. GECCO (Companion) 2019: 17-18 - [c66]Zbynek Pitra, Jakub Repický, Martin Holena:
Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy. GECCO 2019: 691-699 - [c65]Matej Fanta, Petr Pulc, Martin Holena:
Rules Extraction from Neural Networks Trained on Multimedia Data. ITAT 2019: 26-35 - [c64]Jirí Tumpach, Marek Krcál, Martin Holena:
Deep Networks in Online Malware Detection. ITAT 2019: 90-98 - [e1]Petra Barancíková, Martin Holena, Tomás Horváth, Matús Pleva, Rudolf Rosa:
Proceedings of the 19th Conference Information Technologies - Applications and Theory (ITAT 2019), Hotel Zornička, Donovaly, Slovakia, September 20-24, 2019. CEUR Workshop Proceedings 2473, CEUR-WS.org 2019 [contents] - 2018
- [c63]Martin Kopp, Marek Jílek, Martin Holena:
Comparing rule mining approaches for classification with reasoning. ITAT 2018: 52-58 - [c62]Jakub Repický, Martin Holena, Zbynek Pitra:
Automated Selection of Covariance Function for Gaussian process Surrogate Models. ITAT 2018: 64-71 - [c61]Zbynek Pitra, Jakub Repický, Martin Holena:
Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy. ITAT 2018: 72-79 - [c60]Jiri Kozusznik, Petr Pulc, Martin Holena:
Sentiment Analysis from Utterances. ITAT 2018: 92-99 - [c59]Oliver Kerul-Kmec, Petr Pulc, Martin Holena:
Semisupervised Segmentation of UHD Video. ITAT 2018: 100-107 - [c58]Tomás Sabata, Petr Pulc, Martin Holena:
Semi-supervised and Active Learning in Video Scene Classification from Statistical Features. IAL@PKDD/ECML 2018: 24-35 - [c57]Jakub Repický, Zbynek Pitra, Martin Holena:
Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization. IAL@PKDD/ECML 2018: 80-84 - [c56]Zbynek Pitra, Jakub Repický, Martin Holena:
Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization. IAL@PKDD/ECML 2018: 89-94 - [c55]Petr Pulc, Martin Holena:
Hierarchical Motion Tracking Using Matching of Sparse Features. SITIS 2018: 449-456 - 2017
- [c54]Petr Pulc, Martin Holeña:
Towards Real-time Motion Estimation in High-Definition Video Based on Points of Interest. FedCSIS 2017: 67-70 - [c53]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy. GECCO (Companion) 2017: 177-178 - [c52]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Overview of surrogate-model versions of covariance matrix adaptation evolution strategy. GECCO (Companion) 2017: 1622-1629 - [c51]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: 1764-1771 - [c50]Martin Kopp, Matej Nikl, Martin Holena:
Breaking CAPTCHAs with Convolutional Neural Networks. ITAT 2017: 93-99 - [c49]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy. ITAT 2017: 120-128 - [c48]Jakub Puchýr, Martin Holena:
Random-Forest-Based Analysis of URL Paths. ITAT 2017: 129-135 - [c47]Jakub Repický, Lukás Bajer, Zbynek Pitra, Martin Holena:
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models. ITAT 2017: 136-143 - [c46]Tomás Sabata, Tomás Borovicka, Martin Holena:
K-best Viterbi Semi-supervized Active Learning in Sequence Labelling. ITAT 2017: 144-152 - [i5]Jakub Repický, Lukás Bajer, Zbynek Pitra, Martin Holena:
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models. CoRR abs/1709.10443 (2017) - 2016
- [j14]Jan Górecki, 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): 21-59 (2016) - [c45]Gorka Sorrosal, Cruz E. Borges, Martin Holeña, Ana María Macarulla, Cristina Martin Andonegui, Ainhoa Alonso-Vicario:
Evolutionary Dynamic Optimization of Control Trajectories for the Catalytic Transformation of the Bioethanol-To-Olefins Process using Neural Networks. GECCO (Companion) 2016: 133-134 - [c44]Martin Kopp, Matous Pistora, Martin Holena:
How to Mimic Humans, Guide for Computers. ITAT 2016: 110-117 - [c43]Nikita Orekhov, Lukás Bajer, Martin Holena:
Testing Gaussian Process Surrogates on CEC'2013 Multi-Modal Benchmark. ITAT 2016: 138-146 - [c42]Petr Pulc, Erec Rosenzveig, Martin Holena:
Image Processing in Collaborative Open Narrative Systems. ITAT 2016: 155-162 - [c41]Jakub Repický, Lukás Bajer, Martin Holena:
Traditional Gaussian Process Surrogates in the BBOB Framework. ITAT 2016: 163-171 - [c40]Tomás Sabata, Tomás Borovicka, Martin Holena:
Modeling and Clustering the Behavior of Animals Using Hidden Markov Models. ITAT 2016: 172-178 - [c39]Zbynek Pitra, Lukás Bajer, Martin Holena:
Doubly Trained Evolution Control for the Surrogate CMA-ES. PPSN 2016: 59-68 - 2015
- [j13]David Stefka, Martin Holena:
Dynamic classifier aggregation using interaction-sensitive fuzzy measures. Fuzzy Sets Syst. 270: 25-52 (2015) - [j12]Martin Holena, Lukás Bajer, Martin Scavnicky:
Using Copulas in Data Mining Based on the Observational Calculus. IEEE Trans. Knowl. Data Eng. 27(10): 2851-2864 (2015) - [c38]Lukás Bajer, Zbynek Pitra, Martin Holena:
Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed. GECCO (Companion) 2015: 1143-1150 - [c37]Lukás Bajer, Zbynek Pitra, Martin Holena:
Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization. GECCO (Companion) 2015: 1351-1352 - [c36]Lukás Bajer, Martin Holena:
Model Guided Sampling Optimization for Low-dimensional Problems. ICAART (2) 2015: 451-456 - [c35]Vojtech Kopal, Martin Holena:
Comparing Non-Linear Regression Methods on Black-Box Optimization Benchmarks. ITAT 2015: 135-142 - [c34]Martin Kopp, Martin Holena:
Evaluation of Association Rules Extracted during Anomaly Explanation. ITAT 2015: 143-149 - [c33]Michal Kopp, Petr Pulc, Martin Holena:
Search for Structure in Audiovisual Recordings of Lectures and Conferences. ITAT 2015: 150-158 - [c32]Andrej Kudinov, Lukás Bajer, Zbynek Pitra, Martin Holena:
Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization. ITAT 2015: 159-166 - [c31]Zbynek Pitra, Lukás Bajer, Martin Holena:
Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES. ITAT 2015: 186-193 - [i4]Lukás Bajer, Martin Holena:
Model Guided Sampling Optimization for Low-dimensional Problems. CoRR abs/1508.07741 (2015) - 2014
- [c30]Alexandre Adrien Chotard, Martin Holena:
A Generalized Markov-Chain Modelling Approach to (1, λ)-ES Linear Optimization. PPSN 2014: 902-911 - [i3]Alexandre Adrien Chotard, Martin Holena:
A Generalized Markov-Chain Modelling Approach to (1,λ)-ES Linear Optimization: Technical Report. CoRR abs/1406.4619 (2014) - [i2]Lukás Bajer, Martin Holena:
Two Gaussian Approaches to Black-Box Optomization. CoRR abs/1411.7806 (2014) - 2013
- [c29]Lukás Bajer, Viktor Charypar, Martin Holena:
Model guided sampling optimization with gaussian processes for expensive black-box optimization. GECCO (Companion) 2013: 1715-1716 - [c28]Jan Górecki, Martin Holena:
Structure Determination and Estimation of Hierarchical Archimedean Copulas Based on Kendall Correlation Matrix. NFMCP 2013: 132-147 - [c27]Lukás Bajer, Martin Holena:
Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks. SOFSEM 2013: 481-490 - [i1]Martin Holena, Alexandre Adrien Chotard:
A Generalized Markov-Chain Modelling Approach to $(1, λ)$-ES Linear Optimization. CoRR abs/1311.5244 (2013) - 2012
- [j11]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: 55-67 (2012) - [c26]Martin Holena, David Linke, Lukás Bajer:
Surrogate modeling in the evolutionary optimization of catalytic materials. GECCO 2012: 1095-1102 - [c25]Lukás Bajer, Martin Holena:
RBF-based surrogate model for evolutionary optimization. ITAT 2012: 3-8 - [c24]Radim Demut, Martin Holena:
Conformal sets in neural network regression. ITAT 2012: 17-24 - [c23]Martin Holena, David Stefka:
Fuzzy classification rules based on similarity. ITAT 2012: 25-33 - [c22]Viktor Charypar, Martin Holena:
Evolutionary optimization with active learning of surrogate models and fixed evaluation batch. ITAT 2012: 34-40 - 2011
- [c21]Martin Holena, David Linke, Lukás Bajer:
Case study: constraint handling in evolutionary optimization of catalytic materials. GECCO (Companion) 2011: 333-340 - [c20]Martin Holena, Radim Demut:
Assessing the suitability of surrogate models in evolutionary optimization. ITAT 2011: 31-38 - [c19]David Stefka, Martin Holena:
Dynamic Classifier Aggregation Using Fuzzy t-conorm Integral. SITIS 2011: 126-133 - 2010
- [j10]Petr Hájek, Martin Holena, Jan Rauch:
The GUHA method and its meaning for data mining. J. Comput. Syst. Sci. 76(1): 34-48 (2010) - [c18]Martin Holena, David Linke, Uwe Rodemerck, Lukás Bajer:
Neural Networks as Surrogate Models for Measurements in Optimization Algorithms. ASMTA 2010: 351-366 - [c17]Lukás Bajer, Martin Holena:
Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks. IDEAL 2010: 251-258 - [c16]David Stefka, Martin Holena:
Dynamic classifier aggregation using fuzzy integral with interaction-sensitive fuzzy measure. ISDA 2010: 225-230 - [c15]Martin Holena:
Two ways of using artificial neural networks in knowledge discovery from chemical materials data. ITAT 2010: 17-24 - [c14]Martin Holena, David Linke, Uwe Rodemerck:
Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning. SEAL 2010: 220-229
2000 – 2009
- 2009
- [j9]Martin Holena:
Measures of ruleset quality for general rules extraction methods. Int. J. Approx. Reason. 50(6): 867-879 (2009) - [c13]David Stefka, Martin Holena:
Classifier Aggregation using Local Classification Confidence. ICAART 2009: 173-178 - [c12]David Stefka, Martin Holena:
Dynamic Classifier Systems and Their Applications to Random Forest Ensembles. ICANNGA 2009: 458-468 - [c11]Martin Holena, David Linke, Norbert Steinfeldt:
Boosted Neural Networks in Evolutionary Computation. ICONIP (2) 2009: 131-140 - [c10]Martin Holena:
Boosted Surrogate Models in Evolutionary Optimization. ITAT 2009: 15-22 - 2008
- [j8]Martin Holena, Tatjana Cukic, Uwe Rodemerck, David Linke:
Optimization of Catalysts Using Specific, Description-Based Genetic Algorithms. J. Chem. Inf. Model. 48(2): 274-282 (2008) - [c9]Martin Holena:
Measures of quality of rulesets extracted from data. ITAT 2008 - 2007
- [c8]Martin Holena:
Measures of Ruleset Quality Capable to Represent Uncertain Validity. ECSQARU 2007: 430-442 - [c7]David Stefka, Martin Holena:
The Use of Fuzzy t-Conorm Integral for Combining Classifiers. ECSQARU 2007: 755-766 - 2006
- [j7]Martin Holena:
Piecewise-Linear Neural Networks and Their Relationship to Rule Extraction from Data. Neural Comput. 18(11): 2813-2853 (2006) - 2005
- [j6]Martin Holena:
Extraction of fuzzy logic rules from data by means of artificial neural networks. Kybernetika 41(3): 297-314 (2005) - 2004
- [j5]Martin Holena:
Fuzzy hypotheses testing in the framework of fuzzy logic. Fuzzy Sets Syst. 145(2): 229-252 (2004) - [j4]James N. Cawse, Manfred Baerns, Martin Holena:
Efficient Discovery of Nonlinear Dependencies in a Combinatorial Catalyst Data Set. J. Chem. Inf. Model. 44(1): 143-146 (2004) - 2003
- [j3]Petr Hájek, Martin Holena:
Formal logics of discovery and hypothesis formation by machine. Theor. Comput. Sci. 292(2): 345-357 (2003) - [p1]Petr Hájek, Martin Holena, Jan Rauch:
The GUHA Method and Foundations of (Relational) Data Mining. Theory and Applications of Relational Structures as Knowledge Instruments 2003: 17-37 - 2002
- [c6]Martin Holena:
Extraction of Logical Rules from Data by Means of Piecewise-Linear Neural Networks. Discovery Science 2002: 192-205 - 2000
- [c5]Martin Holena:
Observational Logic Integrates Data Mining Based on Statistics and Neural Networks. PKDD 2000: 440-445
1990 – 1999
- 1999
- [c4]Martin Holena, Anna Sochorova, Jana Zvárová:
Increasing the Diversity of Medical Data Mining through Distributed Object Technology. MIE 1999: 442-448 - 1998
- [j2]Martin Holena:
Fuzzy hypotheses for GUHA implications. Fuzzy Sets Syst. 98(1): 101-125 (1998) - [j1]Bernd Blobel, Martin Holena:
CORBA security services for health information systems. Int. J. Medical Informatics 52(1-3): 29-37 (1998) - [c3]Petr Hájek, Martin Holena:
Formal Logics of Discovery and Hypothesis Formation by Machine. Discovery Science 1998: 291-302 - 1994
- [c2]Petra Drescher, Martin Holena, Rainer Kruschinski, Gernod Laufkötter:
Integrating Frames, Rules and Uncertainty in a Database-Coupled Knowledge-Representation System. DEXA 1994: 703-712 - [c1]Martin Holena:
Wahl der Architektur eines neuronalen Netzes mittels der Theorie der Verbände. Fuzzy Days 1994: 365-373
Coauthor Index
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