Product Multi-kernels for Sensor Data Analysis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00444960" target="_blank" >RIV/67985807:_____/15:00444960 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-19324-3_12" target="_blank" >http://dx.doi.org/10.1007/978-3-319-19324-3_12</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-19324-3_12" target="_blank" >10.1007/978-3-319-19324-3_12</a>

Result language
angličtina
Original language name
Product Multi-kernels for Sensor Data Analysis
Original language description
Regularization networks represent a kernel-based model of neural networks with solid theoretical background and a variety of learning possibilities. In this paper, we focus on its extension with multi-kernel units. In particular, we describe the architecture of a product unit network, and we propose an evolutionary learning algorithm for setting its parameters. The algorithm is capable to select different kernels from a dictionary and to set their parameters, including optimal split of inputs into individual products. The approach is tested on real-world data from sensor networks area.
Czech name
—
Czech description
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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

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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