RBF Approximation of Big Data Sets with Large Span of Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43952025" target="_blank" >RIV/49777513:23520/17:43952025 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/MCSI.2017.44" target="_blank" >http://dx.doi.org/10.1109/MCSI.2017.44</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MCSI.2017.44" target="_blank" >10.1109/MCSI.2017.44</a>
Alternative languages
Result language
angličtina
Original language name
RBF Approximation of Big Data Sets with Large Span of Data
Original language description
This contribution presents a new analysis of properties of the Radial Bases Functions (RBF) interpolation and approximation related to data sets with a large data span. The RBF is a convenient method for scattered d-dimensional interpolation and approximation, e.g. for solution of partial differential equations (PDE) etc. The RBF method leads to a solution of linear system of equations and computational complexity of solution is nearly independent of a dimensionality of a problem solved. However, the RBF methods are usually applied for small data sets with a small span of geometric coordinates. In this paper, we show influence of polynomial reproduction mostly used in RBF interpolation and approximation methods in the context of large span data sets. The experiments made proved expected theoretical results.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-05534S" target="_blank" >GA17-05534S: Meshless methods for large scattered spatio-temporal vector data visualization</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Mathematics and Computers in Sciences and in Industry (MCSI), 2017 Fourth International Conference on
ISBN
978-1-5386-2820-1
ISSN
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e-ISSN
neuvedeno
Number of pages
7
Pages from-to
212-218
Publisher name
IEEE
Place of publication
Piscataway
Event location
Corfu, Greece
Event date
Aug 24, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000452189900038