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2020 – today
- 2024
[j9]Juan Carlos Figueroa-García
, Roman Neruda, Germán Jairo Hernández-Pérez:
On cosine fuzzy sets and uncertainty quantification. Eng. Appl. Artif. Intell. 138: 109241 (2024)- [c123]Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter:
Surprisingly Strong Performance Prediction with Neural Graph Features. ICML 2024 - [i3]Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter:
Surprisingly Strong Performance Prediction with Neural Graph Features. CoRR abs/2404.16551 (2024) - 2023
- [j8]Juan Carlos Figueroa-García, Roman Neruda, Germán Jairo Hernández-Pérez:
A genetic algorithm for multivariate missing data imputation. Inf. Sci. 619: 947-967 (2023) - [c122]Juan Carlos Figueroa García, Roman Neruda, Carlos Franco:
On Truncating Fuzzy Numbers with α-Levels. NAFIPS 2023: 258-267 - 2022
- [c121]Gabriela Suchopárová, Petra Vidnerová, Roman Neruda, Martin Smíd:
Using a Deep Neural Network in a Relative Risk Model to Estimate Vaccination Protection for COVID-19. EANN 2022: 310-320 - [c120]Juan Carlos Figueroa García, Roman Neruda, Yurilev Chalco-Cano:
An Experimental Study on Fuzzy Markov Chains Under Mn Generalized Mean Relation. NAFIPS 2022: 63-72 - [p3]Juan Carlos Figueroa-García, Carlos Franco, Roman Neruda:
An Optimization Model for Location-Allocation of Health Services Under Uncertainty. Computational Intelligence Methodologies Applied to Sustainable Development Goals 2022: 97-108 - 2021
- [c119]Alvaro David Orjuela-Cañón, Juan Carlos Figueroa-García, Roman Neruda:
Automated Machine Learning Strategies to Damage Identification of Neurofibromatosis Mutations. ICMLA 2021: 1341-1344 - [c118]Petra Vidnerová, Roman Neruda, Gabriela Suchopárová, Ludek Berec, Tomás Diviák, Ales Kubena, René Levínský, Josef Slerka, Martin Smíd, Jan Trnka, Vít Tucek, Karel Vrbenský, Milan Zajícek:
Simulation of Non-pharmaceutical Interventions in an Agent based Epidemic Model. ITAT 2021: 263-268 - 2020
- [j7]Petra Vidnerová, Roman Neruda:
Vulnerability of classifiers to evolutionary generated adversarial examples. Neural Networks 127: 168-181 (2020) - [c117]Juan Carlos Figueroa-García, Roman Neruda, Yurilev Chalco-Cano, Heriberto Román-Flores:
On the relationship between the centroid and the footprint of uncertainty of Interval Type-2 fuzzy numbers. FUZZ-IEEE 2020: 1-7 - [c116]Petra Vidnerová, Stepán Procházka, Roman Neruda:
Multiobjective Evolution for Convolutional Neural Network Architecture Search. ICAISC (1) 2020: 261-270 - [c115]Petra Vidnerová, Roman Neruda:
Multi-objective Evolution for Deep Neural Network Architecture Search. ICONIP (3) 2020: 270-281 - [c114]Stepán Procházka, Roman Neruda:
Black-box Evolutionary Search for Adversarial Examples against Deep Image Classifiers in Non-Targeted Attacks. IJCNN 2020: 1-8 - [c113]Gabriela Suchopárová, Roman Neruda:
Genens: An AutoML System for Ensemble Optimization Based on Developmental Genetic Programming. SSCI 2020: 631-638
2010 – 2019
- 2019
- [c112]Klára Pesková, Roman Neruda:
Hyperparameters Search Methods for Machine Learning Linear Workflows. ICMLA 2019: 1205-1210 - [c111]Juan Carlos Figueroa-García, Alvaro David Orjuela-Cañón, Roman Neruda:
On the boundaries of the centroid of a class of fuzzy numbers. LA-CCI 2019: 1-5 - 2018
- [c110]Petra Vidnerová, Roman Neruda:
Deep Networks with RBF Layers to Prevent Adversarial Examples. ICAISC (1) 2018: 257-266 - [c109]Petra Vidnerová, Roman Neruda:
Asynchronous Evolution of Convolutional Networks. ITAT 2018: 80-85 - [c108]Lucie Rehakova, Roman Neruda:
Utilization of Genetic Programming to Solve a Simple Task Network Planning Problem. SMC 2018: 3660-3666 - 2017
- [j6]Tomás Kren, Martin Pilát, Roman Neruda:
Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming. Int. J. Artif. Intell. Tools 26(5): 1760020:1-1760020:24 (2017) - [c107]Petra Vidnerová, Roman Neruda:
Evolving KERAS Architectures for Sensor Data Analysis. FedCSIS 2017: 109-112 - [c106]Martin Pilát, Roman Neruda:
Parallel evolutionary algorithm with interleaving generations. GECCO 2017: 865-872 - [c105]Martin Slapák, Roman Neruda:
Matching subtrees in genetic programming crossover operator. ICNC-FSKD 2017: 208-213 - [c104]Josef Moudrík, Tomás Kren, Roman Neruda:
Algorithm Discovery with Monte-Carlo Search: Controlling the Size. ICTAI 2017: 390-395 - [c103]Petra Vidnerová, Roman Neruda:
Evolution Strategies for Deep Neural Network Models Design. ITAT 2017: 159-166 - [c102]Tomás Kren, Josef Moudrík, Roman Neruda:
Combining top-down and bottom-up approaches for automated discovery of typed programs. SSCI 2017: 1-8 - [c101]Tomás Kren, Martin Pilát, Roman Neruda:
Multi-objective evolution of machine learning workflows. SSCI 2017: 1-8 - [c100]Roman Neruda, Martin Pilát, Josef Moudrík:
Unsupervised and Supervised Activity Analysis of Drone Sensor Data. WEA 2017: 3-11 - [e3]Carlos Martín-Vide, Roman Neruda, Miguel A. Vega-Rodríguez:
Theory and Practice of Natural Computing - 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, Proceedings. Lecture Notes in Computer Science 10687, Springer 2017, ISBN 978-3-319-71068-6 [contents] - 2016
- [c99]Petra Vidnerová, Roman Neruda:
Sensor Data Air Pollution Prediction by Kernel Models. CCGrid 2016: 666-673 - [c98]Martin Pilát, Roman Neruda:
General tuning of weights in MOEA/D. CEC 2016: 965-972 - [c97]Martin Pilát, Tomás Kren, Roman Neruda:
Asynchronous Evolution of Data Mining Workflow Schemes by Strongly Typed Genetic Programming. ICTAI 2016: 577-584 - [c96]Petra Vidnerová, Roman Neruda:
Vulnerability of Machine Learning Models to Adversarial Examples. ITAT 2016: 187-194 - [c95]Petra Vidnerová, Roman Neruda:
Evolutionary generation of adversarial examples for deep and shallow machine learning models. MISNC 2016: 43 - [c94]Martin Pilát, Roman Neruda:
Feature Extraction for Surrogate Models in Genetic Programming. PPSN 2016: 335-344 - [c93]Josef Moudrík, Roman Neruda:
Determining Player Skill in the Game of Go with Deep Neural Networks. TPNC 2016: 188-195 - [c92]Roman Neruda:
Search Techniques for Automated Proposal of Data Mining Schemes. WEA 2016: 84-90 - 2015
- [c91]Jakub Smíd, Martin Pilát, Klára Pesková, Roman Neruda:
Co-evolutionary genetic programming for dataset similarity induction. CEC 2015: 1160-1166 - [c90]Josef Moudrík, Petr Baudis, Roman Neruda:
Evaluating Go game records for prediction of player attributes. CIG 2015: 162-168 - [c89]Martin Pilát, Roman Neruda:
Incorporating User Preferences in MOEA/D through the Coevolution of Weights. GECCO 2015: 727-734 - [c88]Petra Vidnerová, Roman Neruda:
Product Multi-kernels for Sensor Data Analysis. ICAISC (1) 2015: 123-133 - [c87]Martin Pilát, Roman Neruda:
Hypervolume-Based Surrogate Model for MO-CMA-ES. ICTAI 2015: 604-611 - [c86]Tomás Kren, Martin Pilát, Klára Pesková, Roman Neruda:
Generating Workflow Graphs Using Typed Genetic Programming. MetaSel@PKDD/ECML 2015: 108-109 - [c85]Tomás Kren, Roman Neruda:
A Dynamic Programming Approach to Individual Initialization in Genetic Programming. SMC 2015: 1752-1757 - [c84]Toma Ken, Martin Pilát, Roman Neruda:
Evolving Workflow Graphs Using Typed Genetic Programming. SSCI 2015: 1407-1414 - [c83]Jakub Smíd, Martin Pilát, Klára Pesková, Roman Neruda:
Multi-Objective Genetic Programming for Dataset Similarity Induction. SSCI 2015: 1576-1582 - [c82]Josef Moudrík, Roman Neruda:
Evolving Non-Linear Stacking Ensembles for Prediction of Go Player Attributes. SSCI 2015: 1673-1680 - [i2]Josef Moudrík, Petr Baudis, Roman Neruda:
Evaluating Go Game Records for Prediction of Player Attributes. CoRR abs/1512.08969 (2015) - [i1]Josef Moudrík, Roman Neruda:
Evolving Non-linear Stacking Ensembles for Prediction of Go Player Attributes. CoRR abs/1512.09254 (2015) - 2014
- [c81]Ondrej Kazík, Roman Neruda:
Data Mining Process Optimization in Computational Multi-agent Systems. ADMI 2014: 93-103 - [c80]Tomás Kren, Roman Neruda:
Generating lambda term individuals in typed genetic programming using forgetful A∗. IEEE Congress on Evolutionary Computation 2014: 1847-1854 - [c79]Martin Pilát, Roman Neruda:
The effect of different local search algorithms on the performance of multi-objective optimizers. IEEE Congress on Evolutionary Computation 2014: 2172-2179 - [c78]Jakub Smíd, Roman Neruda:
Comparing datasets by attribute alignment. CIDM 2014: 56-62 - [c77]Klára Pesková, Jakub Smíd, Martin Pilát, Ondrej Kazík, Roman Neruda:
Hybrid Multi-Agent System for Metalearning in Data Mining. MetaSel@ECAI 2014: 53-54 - [c76]Martin Pilát, Roman Neruda:
Hypervolume-based local search in multi-objective evolutionary optimization. GECCO 2014: 637-644 - [c75]Tomás Kren, Roman Neruda:
Utilization of reductions and abstraction elimination in typed genetic programming. GECCO 2014: 943-950 - [c74]Martin Slapák, Roman Neruda:
Multiobjective Genetic Programming of Agent Decision Strategies. IBICA 2014: 173-182 - 2013
- [j5]Martin Pilát, Roman Neruda:
Aggregate meta-models for evolutionary multiobjective and many-objective optimization. Neurocomputing 116: 392-402 (2013) - [c73]Jan Kohout, Roman Neruda:
Two-Phase Genetic Algorithm for Social Network Graphs Clustering. AINA Workshops 2013: 197-202 - [c72]Martin Pilát, Roman Neruda:
Surrogate model selection for evolutionary multiobjective optimization. IEEE Congress on Evolutionary Computation 2013: 1860-1867 - [c71]Martin Pilát, Roman Neruda:
Multiobjectivization for classifier parameter tuning. GECCO (Companion) 2013: 97-98 - [c70]Jakub Smíd, Roman Neruda:
Using Genetic Programming to Estimate Performance of Computational Intelligence Models. ICANNGA 2013: 169-178 - [c69]Martin Pilát, Roman Neruda:
Multi-objectivization and Surrogate Modelling for Neural Network Hyper-parameters Tuning. ICIC (3) 2013: 61-66 - [c68]Ondrej Kazík, Klára Pesková, Jakub Smíd, Roman Neruda:
Clustering Based Classification in Data Mining Method Recommendation. ICMLA (2) 2013: 356-361 - 2012
- [c67]Ondrej Kazík, Roman Neruda:
Role-Based Management and Matchmaking in Data-Mining Multi-Agent Systems. ADMI 2012: 22-35 - [c66]Stepán Balcar, Martin Pilát, Roman Neruda:
An evolutionary algorithm for 2D semi-guillotinable circular saw cutting. IEEE Congress on Evolutionary Computation 2012: 1-5 - [c65]Martin Pilát, Roman Neruda:
An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization. IEEE Congress on Evolutionary Computation 2012: 1-7 - [c64]Martin Pilát, Roman Neruda:
A surrogate multiobjective evolutionary strategy with local search and pre-selection. GECCO (Companion) 2012: 633-634 - [c63]Ondrej Kazík, Klára Pesková, Martin Pilát, Roman Neruda:
A Novel Meta Learning System and Its Application to Optimization of Computing Agents' Results. IAT 2012: 170-174 - [c62]Roman Neruda, Martin Slapák:
Evolving Decision Strategies for Computational Intelligence Agents. ICIC (2) 2012: 213-220 - [c61]Ondrej Kazík, Klára Pesková, Martin Pilát, Roman Neruda:
Combining Parameter Space Search and Meta-learning for Data-Dependent Computational Agent Recommendation. ICMLA (2) 2012: 36-41 - [c60]Martin Pilát, Roman Neruda:
Meta-learning and Model Selection in Multi-objective Evolutionary Algorithms. ICMLA (1) 2012: 433-438 - [c59]Martin Pilát, Roman Neruda:
A Surrogate Based Multiobjective Evolution Strategy with Different Models for Local Search and Pre-selection. ICTAI 2012: 215-222 - [c58]Jan Kohout, Roman Neruda:
Exploration and Exploitation Operators for Genetic Graph Clustering Algorithm. ISMIS 2012: 87-92 - [p2]Ondrej Kazík, Roman Neruda:
Management of MAS by Means of Automated Reasoning in the Role Model. Software Agents, Agent Systems and Their Applications 2012: 309-322 - 2011
- [c57]Martin Pilát, Roman Neruda:
ASM-MOMA: Multiobjective memetic algorithm with aggregate surrogate model. IEEE Congress on Evolutionary Computation 2011: 1202-1208 - [c56]Martin Pilát, Roman Neruda:
LAMM-MMA: multiobjective memetic algorithm with local aggregate meta-model. GECCO (Companion) 2011: 79-80 - [c55]Martin Pilát, Roman Neruda:
Improving many-objective optimizers with aggregate meta-models. HIS 2011: 555-560 - [c54]Ondrej Kazík, Klára Pesková, Martin Pilát, Roman Neruda:
Meta Learning in Multi-agent Systems for Data Mining. IAT 2011: 433-434 - [c53]Petra Vidnerová, Roman Neruda:
Evolving Sum and Composite Kernel Functions for Regularization Networks. ICANNGA (1) 2011: 180-189 - [c52]Martin Pilát, Roman Neruda:
Local Meta-models for ASM-MOMA. ICIC (3) 2011: 79-84 - [c51]Martin Pilát, Roman Neruda:
Local Meta-models for ASM-MOMA. ICIC (1) 2011: 147-152 - [c50]Ondrej Kazík, Roman Neruda:
Role Model of Search in Agents' Parameter-Space. ICMLA (2) 2011: 31-34 - [c49]Ondrej Kazík, Klára Pesková, Martin Pilát, Roman Neruda:
Implementation of Parameter Space Search for Meta Learning in a Data-Mining Multi-agent System. ICMLA (2) 2011: 366-369 - [c48]Stanislav Slusny, Roman Neruda:
Local Search Heuristics for Robotic Routing Planner. ISNN (3) 2011: 31-40 - [c47]Petra Vidnerová, Roman Neruda:
Evolutionary Learning of Regularization Networks with Multi-kernel Units. ISNN (1) 2011: 538-546 - [c46]Roman Neruda, Ondrej Kazík:
Modeling data mining processes in computational multi-agent systems. MEDES 2011: 61-67 - [c45]Petra Vidnerová, Roman Neruda:
Evolutionary learning of regularization networks with product kernel units. SMC 2011: 638-643 - 2010
- [j4]Vera Kurková, Roman Neruda, Jan Koutník:
Editorial. Neural Networks 23(4): 465 (2010) - [j3]Stanislav Slusny, Roman Neruda, Petra Vidnerová:
Comparison of behavior-based and planning techniques on the small robot maze exploration problem. Neural Networks 23(4): 560-567 (2010) - [c44]Martin Pilát, Roman Neruda:
Combining multiobjective and single-objective genetic algorithms in heterogeneous island model. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c43]Roman Neruda, Petra Vidnerová:
Genetic Algorithm with Species for Regularization Network Metalearning. IAIT 2010: 192-201 - [c42]Petra Vidnerová, Roman Neruda:
Hybrid Learning of Regularization Neural Networks. ICAISC (2) 2010: 124-131 - [c41]Stanislav Slusny, Michal Zerola, Roman Neruda:
Real Time Robot Path Planning and Cleaning. ICIC (2) 2010: 442-449 - [c40]Roman Neruda, Petra Vidnerová:
Memetic Evolutionary Learning for Local Unit Networks. ISNN (1) 2010: 534-541 - [c39]Roman Neruda, Ondrej Kazík:
Role-based design of computational intelligence multi-agent system. MEDES 2010: 95-101
2000 – 2009
- 2009
- [j2]Roman Vaculín, Roman Neruda, Katia P. Sycara:
The process mediation framework for semantic web services. Int. J. Agent Oriented Softw. Eng. 3(1): 27-58 (2009) - [c38]Roman Neruda:
Description, Composition, and Decision Support for Multiagent Computational Systems. ICTAI 2009: 300-307 - [c37]Stanislav Slusny, Roman Neruda, Petra Vidnerová:
Localization With a Low-cost Robot. ITAT 2009: 77-80 - [p1]Roman Neruda:
Towards Data-Driven Hybrid Composition of Data Mining Multi-agent Systems. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2009: 271-281 - 2008
- [c36]Roman Vaculín, Roman Neruda, Katia P. Sycara:
Towards Extending Service Discovery with Automated Composition Capabilities. ECOWS 2008: 3-12 - [c35]Roman Neruda, Stanislav Slusny, Petra Vidnerová:
Evolutionary Learning and Behavior Analysis of the Autonomous Robot Neuro-Controller. GEM 2008: 222-228 - [c34]Roman Neruda:
Hybrid Search Methods for Automatic Discovery of Computational Agent Schemes. Web Intelligence/IAT Workshops 2008: 579-582 - [c33]Roman Neruda, Václav Snásel, Jan Platos, Pavel Krömer, Dusan Húsek, Alexander A. Frolov:
Implementing Boolean Matrix Factorization. ICANN (1) 2008: 543-552 - [c32]Stanislav Slusny, Roman Neruda, Petra Vidnerová:
Comparison of RBF Network Learning and Reinforcement Learning on the Maze Exploration Problem. ICANN (1) 2008: 720-729 - [c31]Petra Vidnerová, Stanislav Slusny, Roman Neruda:
Evolutionary trained radial basis function networks for robot control. ICARCV 2008: 833-838 - [c30]Stanislav Slusny, Roman Neruda, Petra Vidnerová:
Rule-Based Analysis of Behaviour Learned by Evolutionary and Reinforcement Algorithms. ICIC (2) 2008: 284-291 - [c29]Roman Vaculín, Huajun Chen, Roman Neruda, Katia P. Sycara:
Modeling and Discovery of Data Providing Services. ICWS 2008: 54-61 - [c28]Petra Vidnerová, Roman Neruda:
Testing Error Estimates for Regularization and Radial Function Networks. ISNN (1) 2008: 549-554 - [c27]Stanislav Slusny, Roman Neruda, Petra Vidnerová:
Learning algorithms for small mobile robots: case study on maze exploration. ITAT 2008 - [c26]Roman Neruda:
Ontology-based and Evolutionary Search for Computational Agents Schemes. SEKE 2008: 569-572 - [c25]Roman Vaculín, Roman Neruda, Katia P. Sycara:
An Agent for Asymmetric Process Mediation in Open Environments. SOCASE 2008: 104-117 - [e2]Vera Kurková, Roman Neruda, Jan Koutník:
Artificial Neural Networks - ICANN 2008 , 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part I. Lecture Notes in Computer Science 5163, Springer 2008, ISBN 978-3-540-87535-2 [contents] - [e1]Vera Kurková, Roman Neruda, Jan Koutník:
Artificial Neural Networks - ICANN 2008, 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II. Lecture Notes in Computer Science 5164, Springer 2008, ISBN 978-3-540-87558-1 [contents] - 2007
- [c24]Roman Neruda:
Evolving neural network which control a robotic agent. IEEE Congress on Evolutionary Computation 2007: 1517-1522 - [c23]Roman Neruda:
Hybrid evolutionary algorithm for multilayer perceptron networkswith competitive performance. IEEE Congress on Evolutionary Computation 2007: 1620-1627 - [c22]Roman Neruda, Stanislav Slusny:
Variants of Memetic And Hybrid Learning of Perceptron Networks. DEXA Workshops 2007: 158-162 - 2006
- [c21]Suhail S. J. Owais, Pavel Krömer, Václav Snásel, Dusan Húsek, Roman Neruda:
Implementing GP on Optimizing both Boolean and Extended Boolean Queries in IR and Fuzzy IR systems with Respect to the Users Profiles. IEEE Congress on Evolutionary Computation 2006: 1499-1505 - [c20]Roman Neruda:
Cooperation of Computational Intelligence Agents. CTS 2006: 256-263 - [c19]Andrei Kursin, Dusan Húsek, Roman Neruda:
Faster Learning with Overlapping Neural Assemblies. ICANN (1) 2006: 226-233 - [c18]Roman Neruda:
Emerging Hybrid Computational Models. ICIC (2) 2006: 379-389 - [c17]Roman Neruda, Gerd Beuster:
Description and Generation of Computational Agents. KSEM 2006: 318-329 - 2005
- [j1]Roman Neruda, Petra Kudová:
Learning methods for radial basis function networks. Future Gener. Comput. Syst. 21(7): 1131-1142 (2005) - [c16]Roman Neruda, Pavel Krusina:
Estimating and Measuring Performance of Computational Agents. IAT 2005: 615-618 - 2004
- [c15]Petra Kudová, Roman Neruda:
Kernel Based Learning Methods: Regularization Networks and RBF Networks. Deterministic and Statistical Methods in Machine Learning 2004: 124-136 - [c14]Roman Neruda, Pavel Krusina, Petra Kudová, Pavel Rydvan, Gerd Beuster:
Bang 3: A Computational Multi-Agent System. IAT 2004: 563-564 - 2003
- [c13]Gerd Beuster, Pavel Krusina, Roman Neruda, Pavel Rydvan:
Towards building computational agent schemes. ICANNGA 2003: 210-215 - [c12]Roman Neruda:
Building Hybrid Soft Computing Agents. Neural Networks and Computational Intelligence 2003: 7-12 - 2002
- [c11]Roman Neruda, Arnost Stedrý, Jitka Drkosová:
Variants of Learning Algorithm Based on Kolmogorov Theorem. International Conference on Computational Science (3) 2002: 536-543 - [c10]Roman Neruda, Petra Kudová:
Hybrid Learning of RBF Networks. International Conference on Computational Science (3) 2002: 594-603 - 2001
- [c9]Roman Neruda, Pavel Krusina, Zuzana Petrová:
More Autonomous Hybrid Models in Bang. International Conference on Computational Science (2) 2001: 935-942 - [c8]Roman Neruda, Arnost Stedrý, Jitka Drkosová:
Implementation of Kolmogorov Learning Algorithm for Feedforward Neural Networks. International Conference on Computational Science (2) 2001: 986-995 - [c7]Roman Neruda:
Agents that Make Hybrid AI Models. ISAS-SCI (1) 2001: 24-29 - 2000
- [c6]Roman Neruda:
Genetic Algorithms and Neural Networks: Making Use of Parameter Space Symmetries. IJCNN (1) 2000: 293-298
1990 – 1999
- 1999
- [c5]Roman Neruda:
Utilizing unique parametrization property in approximate genetic learning of feed-forward networks. IJCNN 1999: 4186-4191 - 1997
- [c4]Roman Neruda:
Canonical Genetic Learning of RBF Networks Is Faster. ICANNGA 1997: 350-353 - [c3]Roman Neruda:
Yet Another Genetic Algorithm for Feed-Forward Neural Networks. ICTAI 1997: 375- - [c2]David Strupl, Roman Neruda:
Parallelizing Self-Organizing Maps. SOFSEM 1997: 563-570 - 1995
- [c1]Roman Neruda:
Functional Equivalence and Genetic Learning of RBF Networks. ICANNGA 1995: 53-56
Coauthor Index
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