Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Petra Vidnerová"'
Autor:
Petra Vidnerová, Jan Kalina
Publikováno v:
Artificial Intelligence and Soft Computing ISBN: 9783031234910
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9575278cecd764da82e712ef1891ed1b
https://doi.org/10.1007/978-3-031-23492-7_13
https://doi.org/10.1007/978-3-031-23492-7_13
Using a Deep Neural Network in a Relative Risk Model to Estimate Vaccination Protection for COVID-19
Publikováno v:
Engineering Applications of Neural Networks ISBN: 9783031082221
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::523bfd78c96012a7f361676b6f438b15
https://doi.org/10.1007/978-3-031-08223-8_26
https://doi.org/10.1007/978-3-031-08223-8_26
Publikováno v:
Artificial Intelligence and Soft Computing ISBN: 9783030879853
ICAISC (1)
ICAISC (1)
Trend estimation, i.e. estimating or smoothing a nonlinear function without any independent variables, belongs to important tasks in various applications within signal and image processing, engineering, biomedicine, analysis of economic time series,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::431cd33c4e97ff8a5e918373e787dd7d
https://doi.org/10.1007/978-3-030-87986-0_8
https://doi.org/10.1007/978-3-030-87986-0_8
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:
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, Roman Neruda
Publikováno v:
Neural Information Processing ISBN: 9783030638351
ICONIP (3)
ICONIP (3)
In this paper, we propose a multi-objective evolutionary algorithm for automatic deep neural architecture search. The algorithm optimizes the performance of the model together with the number of network parameters. This allows exploring architectures
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d9528e0cb0daead0a5a4fc08552e512
https://doi.org/10.1007/978-3-030-63836-8_23
https://doi.org/10.1007/978-3-030-63836-8_23
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:
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:
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