Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Petra Vidnerová"'
Autor:
Jan Kalina, Petra Vidnerová
Publikováno v:
Functional and High-Dimensional Statistics and Related Fields ISBN: 9783030477554
Estimation, prediction or smoothing of curves represents a fundamental task of functional data analysis. Nonlinear regression methods allow to search for the best-fit curves explaining the dependence of a response variable on available independent va
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8dca978ab2735eade6d0503c4908c53
https://doi.org/10.1007/978-3-030-47756-1_20
https://doi.org/10.1007/978-3-030-47756-1_20
Autor:
Petra Vidnerová, Jan Kalina
Publikováno v:
Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference ISBN: 9783030487904
EANN
EANN
Common types of artificial neural networks have been well known to suffer from the presence of outlying measurements (outliers) in the data. However, there are only a few available robust alternatives for training common form of neural networks. In t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7b5e8e58333091f108366811fe9c74e7
https://doi.org/10.1007/978-3-030-48791-1_43
https://doi.org/10.1007/978-3-030-48791-1_43
Publikováno v:
Artificial Intelligence and Soft Computing ISBN: 9783030614003
ICAISC (1)
ICAISC (1)
The choice of an architecture is crucial for the performance of the neural network, and thus automatic methods for architecture search have been proposed to provide a data-dependent solution to this problem. In this paper, we deal with an automatic n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d0deaf599890f75ed9971ff4284f7aa
https://doi.org/10.1007/978-3-030-61401-0_25
https://doi.org/10.1007/978-3-030-61401-0_25
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:
Jan Kalina, Petra Vidnerová
Publikováno v:
Artificial Intelligence and Soft Computing ISBN: 9783030209117
ICAISC (1)
ICAISC (1)
Radial basis function (RBF) neural networks represent established machine learning tool with various interesting applications to nonlinear regression modeling. However, their performance may be substantially influenced by outlying measurements (outli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8dea7d41cea6672b2d13abe7cac6c759
https://doi.org/10.1007/978-3-030-20912-4_11
https://doi.org/10.1007/978-3-030-20912-4_11
Autor:
Roman Neruda, Petra Vidnerová
Publikováno v:
Artificial Intelligence and Soft Computing ISBN: 9783319912523
ICAISC (1)
ICAISC (1)
We propose a simple way to increase the robustness of deep neural network models to adversarial examples. The new architecture obtained by stacking deep neural network and RBF network is proposed. It is shown on experiments that such architecture is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::edb8ca85705a722831c21a72feaf1529
https://doi.org/10.1007/978-3-319-91253-0_25
https://doi.org/10.1007/978-3-319-91253-0_25
Autor:
Roman Neruda, Petra Vidnerová
Publikováno v:
FedCSIS
Deep neural networks enjoy high interest and have become the state-of-art methods in many fields of machine learning recently. Still, there is no easy way for a choice of network architecture. However, the choice of architecture can significantly inf
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,
Publikováno v:
Neural Networks. 23:560-567
A comparison of behavior-based and planning approaches of robot control is presented in this paper. We focus on miniature mobile robotic agents with limited sensory abilities. Two reactive control mechanisms for an agent are considered-a radial basis