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Neural Network Detectors for Composite Hypothesis Tests.

, , , , and . IDEAL, volume 4224 of Lecture Notes in Computer Science, page 298-305. Springer, (2006)

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Enlarging Training Sets for Neural Networks., , , and . ANNs, page 25-33. INSTICC Press, (2004)On the Capability of Neural Networks to Approximate the Neyman-Pearson Detector: A Theoretical Study., , , and . ANNs, page 69-76. INSTICC Press, (2004)Speech source separation using a generalized mean shift algorithm., , , and . Signal Processing, 92 (9): 2248-2252 (2012)Robustness of a Generalized Gamma CFAR ship detector applied to TerraSAR-X and Sentinel-1 images., , , , and . EUROCON, page 1-6. IEEE, (2015)A neural network approach to improve radar detector robustness., , , , and . EUSIPCO, page 1-5. IEEE, (2006)Design of a Pre-processing Stage for Avoiding the Dependence on TSNR of a Neural Radar Detector., , and . IWANN (2), volume 2085 of Lecture Notes in Computer Science, page 652-659. Springer, (2001)SONN and MLP Based Solutions for Detecting Fluctuating Targets with Unknown Doppler Shift in Gaussian Interference., , , , and . IWANN (1), volume 7902 of Lecture Notes in Computer Science, page 584-591. Springer, (2013)Using Multilayer Perceptrons to Align High Range Resolution Radar Signals., , , and . ICANN (2), volume 3697 of Lecture Notes in Computer Science, page 911-916. Springer, (2005)MLP and RBFN for Detecting White Gaussian Signals in White Gaussian Interference., , , and . IWANN (2), volume 2687 of Lecture Notes in Computer Science, page 790-797. Springer, (2003)Doppler Processors as Suboptimum Approaches for Detecting Targets with Unknown Doppler Shift., , , , and . CICSyN, page 283-288. IEEE, (2013)