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.
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Vidnerová, P., Neruda, R. (2015). Product Multi-kernels for Sensor Data Analysis. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_12
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DOI: https://doi.org/10.1007/978-3-319-19324-3_12
Publisher Name: Springer, Cham
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