Performance Analysis of Neural Network Detectors by Importance Sampling Techniques
Neural Processing Letters
Mixture density modeling, Kullback-Leibler divergence, and differential log-likelihood
Signal Processing - Special issue: Information theoretic signal processing
Advances in Neyman-Pearson Neural Detectors Design
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
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This paper is devoted to the design of a neural alternative to binary detectors optimized in the Neyman-Pearson sense. These detectors present a configurable low probability of classifying binary symbol 1 when symbol O is the correct decision. This kind of error, referred in the scientific literature as false-posetive or false a l a m probability has a high cost in many real applications as medical Computer Aided Diagnosis or Radar and Sonar Target Detection, and the possibility of controlling its maximum value is crucial. The novelty and interest of the detector is the application of a Multilayer Perceptron instead of a classical design. Under some conditions, the Neural Detector presents a performance competitive with classical designs adding the typical advantages of Neural Networks. So, the presented Neural Detectors may be considered as an alternative to classical ones.