Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Petra Vidnerová"'
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
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:
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