Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Petra Vidnerová"'
Autor:
Petra Vidnerová, Roman Neruda
Publikováno v:
Modelling, Vol 2, Iss 35, Pp 659-674 (2021)
Modelling
Volume 2
Issue 4
Pages 35-674
Modelling
Volume 2
Issue 4
Pages 35-674
Precise environmental modelling of pollutants distributions represents a key factor for addresing the issue of urban air pollution. Nowadays, urban air pollution monitoring is primarily carried out by employing sparse networks of spatially distribute
Publikováno v:
International journal of neural systems. 31(10)
Metalearning, an important part of artificial intelligence, represents a promising approach for the task of automatic selection of appropriate methods or algorithms. This paper is interested in recommending a suitable estimator for nonlinear regressi
Publikováno v:
Analytical Methods in Statistics ISBN: 9783030488130
The methodology of automatic method selection (metalearning) allows to recommend the most suitable method (e.g. algorithm or statistical estimator) from several alternatives for a given dataset, based on information learned over a training database o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5688a30e5ac4cce1c5807fdca92c778a
https://doi.org/10.1007/978-3-030-48814-7_7
https://doi.org/10.1007/978-3-030-48814-7_7
Autor:
Jan Kalina, Petra Vidnerová
Publikováno v:
Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference ISBN: 9783030487904
EANN
EANN
Metalearning is a methodology aiming at recommending the most suitable algorithm (or method) from several alternatives for a particular dataset. Its classification rule is learned over an available training database of datasets. It gradually penetrat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fff24f4aad17a6b7d29112e65f7076a2
https://doi.org/10.1007/978-3-030-48791-1_39
https://doi.org/10.1007/978-3-030-48791-1_39
Autor:
Roman Neruda, Petra Vidnerová
Publikováno v:
Neural networks : the official journal of the International Neural Network Society. 127
This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine l
Autor:
Petra Vidnerová
Publikováno v:
Genetic Programming and Evolvable Machines. 20:151-153
Autor:
Petra Vidnerová, Roman Neruda
Publikováno v:
MISNC
Studying vulnerability of machine learning models to adversarial examples is an important way to understand their robustness and generalization properties. In this paper, we propose a genetic algorithm for generating adversarial examples for machine
Autor:
Roman Neruda, Petra Vidnerová
Publikováno v:
CCGrid
Kernel-based neural networks are popular machine learning approach with many successful applications. Regularization networks represent a their special subclass with solid theoretical background and a variety of learning possibilities. In this paper,
Autor:
Roman Neruda, Petra Vidnerová
Publikováno v:
Artifical Intelligence and Soft Computing ISBN: 9783642132315
ICAISC (2)
ICAISC (2)
Regularization theory presents a sound framework to solving supervised learning problems. However, the regularization networks have a large size corresponding to the size of training data. In this work we study a relationship between network complexi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a60a8443a9d11b5a0b276107a642fa5
https://doi.org/10.1007/978-3-642-13232-2_15
https://doi.org/10.1007/978-3-642-13232-2_15
Publikováno v:
2008 Second International Conference on Future Generation Communication and Networking Symposia.
A performance of two learning mechanisms for small mobile robots is performed in this paper. Relational reinforcement learning, and radial basis function neural network learned by evolutionary algorithm are trained to perform the same maze exploratio