Zobrazeno 21 - 30
of 45
pro vyhledávání: '"Petra Vidnerová"'
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:
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
Autor:
Roman Neruda, Petra Vidnerová
Publikováno v:
Adaptive and Natural Computing Algorithms ISBN: 9783642202810
ICANNGA (1)
ICANNGA (1)
In this paper we propose a novel evolutionary algorithm for regularization networks. The main drawback of regularization networks in practical applications is the presence of meta-parameters, including the type and parameters of kernel functions Our
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9947a92fd2b2bb1fba8da2730ba2856a
https://doi.org/10.1007/978-3-642-20282-7_19
https://doi.org/10.1007/978-3-642-20282-7_19
Autor:
Roman Neruda, Petra Vidnerová
Publikováno v:
Advances in Neural Networks – ISNN 2011 ISBN: 9783642211041
ISNN (1)
ISNN (1)
This paper deals with learning possibilities of regularization networks with product kernel units. Approximation problems formulated as regularized minimization problems with kernel-based stabilizers lead to solutions of the shape of linear combinati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::584cc830f0538fba59dcfbca742ede9c
https://doi.org/10.1007/978-3-642-21105-8_62
https://doi.org/10.1007/978-3-642-21105-8_62