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
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:
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:
Roman Neruda, Petra Vidnerová
Publikováno v:
Advances in Information Technology ISBN: 9783642166983
IAIT
IAIT
Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, though their drawback is the presence of metaparameters. The metaparameters are typically supposed to be given by an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be9ed1bc4828e1abd07fe7b001d3ebae
https://doi.org/10.1007/978-3-642-16699-0_21
https://doi.org/10.1007/978-3-642-16699-0_21