Comparing Fixed and Variable-Width Gaussian Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00428366" target="_blank" >RIV/67985807:_____/14:00428366 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.neunet.2014.05.005" target="_blank" >http://dx.doi.org/10.1016/j.neunet.2014.05.005</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neunet.2014.05.005" target="_blank" >10.1016/j.neunet.2014.05.005</a>
Alternative languages
Result language
angličtina
Original language name
Comparing Fixed and Variable-Width Gaussian Networks
Original language description
The role of width of Gaussians in two types of computational models is investigated: Gaussian radial basis- functions (RBFs) where both widths and centers vary and Gaussian kernel networks which have fixed widths but varying centers. The effect of widthon functional equivalence, universal approximation property, and form of norms in reproducing kernel Hilbert spaces (RKHSs)is explored. It is proven that if two Gaussian RBF networks have the same input?output functions, then they must have the same numbers of units with the same centers and widths. Further, it is shown that while sets of input?output functions of Gaussian kernel networks with two different widths are disjoint, each such set is large enough to be a universal approximator. Embedding of RKHSs induced by flatter?? Gaussians into RKHSs induced by sharper?? Gaussians is described and growth of the ratios of norms on these spaces with increasing input dimension is estimated. Finally, large sets of argminima of error functiona
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/LD13002" target="_blank" >LD13002: Modeling of complex systems for softcomputing methods</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Networks
ISSN
0893-6080
e-ISSN
—
Volume of the periodical
57
Issue of the periodical within the volume
September
Country of publishing house
GB - UNITED KINGDOM
Number of pages
6
Pages from-to
23-28
UT code for WoS article
000340319400003
EID of the result in the Scopus database
—