Evolving Keras Architectures for Sensor Data Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00478496" target="_blank" >RIV/67985807:_____/17:00478496 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.15439/2017F241" target="_blank" >http://dx.doi.org/10.15439/2017F241</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.15439/2017F241" target="_blank" >10.15439/2017F241</a>
Alternative languages
Result language
angličtina
Original language name
Evolving Keras Architectures for Sensor Data Analysis
Original language description
Deep neural networks enjoy high interest and have become the state-of-art methods in many fields of machine learning recently. Still, there is no easy way for a choice of network architecture. However, the choice of architecture can significantly influence the network performance. This work is the first step towards an automatic architecture design. We propose a genetic algorithm for an optimization of a network architecture. The algorithm is inspired by and designed directly for the Keras library [1] that is one of the most common implementations of deep neural networks. The target application is the prediction of air pollution based on sensor measurements. The proposed algorithm is evaluated on experiments on sensor data and compared to several fixed architectures and support vector regression.
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/GA15-18108S" target="_blank" >GA15-18108S: Model complexity of neural, radial, and kernel networks</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
Proceedings of the 2017 Federated Conference on Computer Science and Information Systems
ISBN
978-83-946253-7-5
ISSN
2300-5963
e-ISSN
—
Number of pages
4
Pages from-to
109-112
Publisher name
Polish Information Processing Society
Place of publication
Warszawa
Event location
Prague
Event date
Sep 3, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000417412800015