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pro vyhledávání: '"Petra Vidnerová"'
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:
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:
2008 Second International Conference on Future Generation Communication and Networking Symposia.
There is a gap between the theoretical results of regularization theory and practical suitability of regularization-derived networks (RN). On the other hand, radial basis function networks (RBF) that can be seen as a special case of regularization ne
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
Publikováno v:
ICARCV
An emergence of intelligent behaviour within a simple robotic agent is studied in this paper. The radial basis function neural network is used as the control mechanism of the robot. Evolutionary algorithm is used to train the agent to perform several