Sensor Data Air Pollution Prediction by Kernel Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F16%3A00462723" target="_blank" >RIV/67985807:_____/16:00462723 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CCGrid.2016.80" target="_blank" >http://dx.doi.org/10.1109/CCGrid.2016.80</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CCGrid.2016.80" target="_blank" >10.1109/CCGrid.2016.80</a>
Alternative languages
Result language
angličtina
Original language name
Sensor Data Air Pollution Prediction by Kernel Models
Original language description
Kernel-based neural networks are popular machine learning approach with many successful applications. Regularization networks represent a their special subclass with solid theoretical background and a variety of learning possibilities. In this paper, we focus on single and multi-kernel units, in particular, we describe the architecture of a product unit network, and describe an evolutionary learning algorithm for setting its parameters including different kernels from a dictionary, and optimal split of inputs into individual products. The approach is tested on real-world data from calibration of air-pollution sensor networks, and the performance is compared to several different regression tools.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA15-18108S" target="_blank" >GA15-18108S: Model complexity of neural, radial, and kernel networks</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings og the 16th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing
ISBN
978-1-5090-2453-7
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
666-673
Publisher name
IEEE CS
Place of publication
Los Alamitos
Event location
Cartagena de Indias
Event date
May 16, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000382529800091