Neutron-Gamma Classification by Evolutionary Fuzzy Rules and Support Vector Machines

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@InProceedings{Kroemer:2015:ieeeSMC,

• author = "Pavel Kroemer and Zdenek Matej and Petr Musilek and Vaclav Prenosil and Frantiek Cvachovec",
• booktitle = "2015 IEEE International Conference on Systems, Man, and Cybernetics",
• title = "Neutron-Gamma Classification by Evolutionary Fuzzy Rules and Support Vector Machines",
• year = "2015",
• pages = "2638--2642",
• abstract = "Accurate and fast methods for neutron-gamma discrimination play an essential role in the development of digital scintillation detectors. Digital detectors allow the use of state-of-the-art data analysis, mining, and classification methods in place of traditional approaches based on analogue technology such as the pulse rise-time and charge-comparison methods. This work compares the ability of evolutionary fuzzy rules and support vector machines to perform accurate neutron-gamma classification. The accuracy and performance of both investigated methods are evaluated on two real-world data sets.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/SMC.2015.461",
• month = oct,
• notes = "Also known as \cite{7379593}",

}

Genetic Programming entries for Pavel Kroemer Zdenek Matej Petr Musilek Vaclav Prenosil Frantiek Cvachovec

Citations