Sensor Data Air Pollution Prediction by Kernel Models
Result 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.
Keywords
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
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
GA15-18108S: Model complexity of neural, radial, and kernel networks
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
Basic information
Result type
D - Article in proceedings
CEP
IN - Informatics
Year of implementation
2016