Abstract
Nowadays, parallelization is an increasingly popular tool to speed up algorithms. Data classification is one of the many fields of computer science that can take significant advantage of that. In this paper, a parallel implementation of Fuzzy RBF based filters are proposed for pattern recognition problems. It realizes a simple pattern matching by using the radial basis functions for proximity detection, then simply choosing the class or label associated to the pattern as output. The classifier has the advantage of being very simple to implement, to train and to modify the obtained knowledge. With the parallel computing improvement, the speed of both the training and evaluation phase are significantly increased compared to the sequential implementation.
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Acknowledgements
This work has partially been sponsored by the Research & Development Operational Program for the project “Modernization and Improvement of Technical Infrastructure for Research and Development of J. Selye University in the Fields of Nanotechnology and Intelligent Space”, ITMS 26210120042, co-funded by the European Regional Development Fund.
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Tusor, B., Várkonyi-Kóczy, A.R., Bukor, J. (2019). A Parallel Fuzzy Filter Network for Pattern Recognition. In: Laukaitis, G. (eds) Recent Advances in Technology Research and Education. INTER-ACADEMIA 2018. Lecture Notes in Networks and Systems, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-319-99834-3_36
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DOI: https://doi.org/10.1007/978-3-319-99834-3_36
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