Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Petra Vidnerová"'
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:
Artifical Intelligence and Soft Computing ISBN: 9783642132315
ICAISC (2)
ICAISC (2)
Regularization theory presents a sound framework to solving supervised learning problems. However, the regularization networks have a large size corresponding to the size of training data. In this work we study a relationship between network complexi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a60a8443a9d11b5a0b276107a642fa5
https://doi.org/10.1007/978-3-642-13232-2_15
https://doi.org/10.1007/978-3-642-13232-2_15
Autor:
Petra Vidnerová, Roman Neruda
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540877318
ISNN (1)
ISNN (1)
Regularization theory presents a sound framework to solving supervised learning problems. However, there is a gap between the theoretical results and practical suitability of regularization networks (RN). Radial basis function networks (RBF) can be s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c631ab6c015615bf4645658756a00a53
https://doi.org/10.1007/978-3-540-87732-5_61
https://doi.org/10.1007/978-3-540-87732-5_61