Product Multi-kernels for Sensor Data Analysis

0444960 - ÚI 2016 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
Vidnerová, Petra - Neruda, Roman
Product Multi-kernels for Sensor Data Analysis.
Artificial Intelligence and Soft Computing. Vol. 1. Cham: Springer, 2015 - (Rutkowski, L.; Korytkowski, M.; Scherer, R.; Tadeusiewicz, R.; Zadeh, L.; Zurada, J.), s. 123-133. Lecture Notes in Artificial Intelligence, 9119. ISBN 978-3-319-19323-6. ISSN 0302-9743.
[ICAISC 2015. International Conference on Artificial Intelligence and Soft Computing /14./. Zakopane (PL), 12.06.2015-16.06.2015]
Grant CEP: GA ČR GA15-18108S
Institucionální podpora: RVO:67985807
Klíčová slova: Regularization networks * Multi-kernel models * Product units * Sensor data
Kód oboru RIV: IN - Informatika

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.
Trvalý link: http://hdl.handle.net/11104/0247392