Approximating the Neyman-Pearson detector with 2C-SVMs. Application to radar detection - researchr publication related

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David de la Mata-Moya, María-Pilar Jarabo-Amores, Jaime Martin-de-Nicolas, Manuel Rosa-Zurera. Approximating the Neyman-Pearson detector with 2C-SVMs. Application to radar detection. Signal Processing, 131:364-375, 2017. [doi]

The following publications are possibly variants of this publication:

On the Capability of Neural Networks to Approximate the Neyman-Pearson Detector: A Theoretical StudyPilar Jarabo Amores, Roberto Gil-Pita, Manuel Rosa-Zurera, Francisco López-Ferreras. anns 2004: 69-76

Sensitivity of neural networks which approximate the Neyman-Pearson detector to threshold variationsPilar Jarabo Amores, Roberto Gil-Pita, Manuel Rosa-Zurera, Francisco López-Ferreras. eusipco 2005: 1-4 [doi]

MLP-based approximation to the Neyman Pearson detector in a terrestrial passive bistatic radar scenarioNerea del-Rey-Maestre, David de la Mata-Moya, Pilar Jarabo Amores, Jaime Martin-de-Nicolas, Pedro Gomez-del-Hoyo. eurocon 2015: 1-6 [doi]

Approximating the Neyman-Pearson Detector for Swerling I Targets with Low Complexity Neural NetworksD. de la Mata-Moya, Pilar Jarabo Amores, Manuel Rosa-Zurera, Francisco López-Ferreras, Raul Vicen-Bueno. icann 2005: 917-922 [doi]

Study of two error functions to approximate the Neyman-pearson detector using supervised learning machinesMaría-Pilar Jarabo-Amores, Manuel Rosa-Zurera, Roberto Gil-Pita, Francisco López-Ferreras. tsp, 57(11):4175-4181, 2009. [doi]

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