Název: Capabilities of Radial and Kernel Networks
Autoři: Kůrková, Věra
Typ dokumentu: Příspěvky z konference
Konference/Akce: MENDEL 2013. International Conference on Soft Computing /19./, Brno (CZ), 2013-06-26 / 2013-06-28
Rok: 2013
Jazyk: eng
Abstrakt: Originally, artificial neural networks were built from biologically inspired units called perceptrons. Later, other types of units became popular in neurocomputing due to their good mathematical properties. Among them, radial-basis-function (RBF) units and kernel units became most popular. The talk will discuss advantages and limitations of networks with these two types of computational units. Higher flexibility in choice of free parameters in RBF will be compared with benefits of geometrical properties of kernel models allowing applications of maximal margin classification algorithms, modelling of generalization in learning from data in terms of regularization, and characterization of optimal solutions of learning tasks. Critical influence of input dimension on behavior of these two types of networks will be described. General results will be illustrated by the paradigmatic examples of Gaussian kernel and radial networks.
Klíčová slova: advantages and limitations of networks; artificial neural networks; Gaussian kernel and radial networks; kernel units; radial-basis-function
Číslo projektu: LD13002 (CEP)
Poskytovatel projektu: GA MŠk
Zdrojový dokument: MENDEL 2013, ISBN 978-80-214-4755-4, ISSN 1803-3814

Instituce: Ústav informatiky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný v příslušném ústavu Akademie věd ČR.
Původní záznam: http://hdl.handle.net/11104/0224596

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-156653


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Konferenční materiály > Příspěvky z konference