Product Multi-kernels for Sensor Data Analysis

Authors: Petra Vidnerová, Roman Neruda
Publication year: 2015
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: Artificial Intelligence and Soft Computing
Projects: Model complexity of neural, radial, and kernel networks
Bibliographic citation: VIDNEROVÁ, Petra, NERUDA, Roman. Product Multi-kernels for Sensor Data Analysis. In: RUTKOWSKI, L., KORYTKOWSKI, M., SCHERER, R., TADEUSIEWICZ, R., ZADEH, L.A., ZURADA, J.M., eds. Artificial Intelligence and Soft Computing. Vol. 1. Cham: Springer, 2015, S. 123-133. Lecture Notes in Artificial Intelligence, 9119. ISBN 978-3-319-19323-6. ISSN 0302-9743
Dostupné z: doi:10.1007/978-3-319-19324-3_12
DOI: 10.1007/978-3-319-19324-3_12
ID: 444960

Abstract

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.