Performance Analysis of Neural Network Detectors by Importance Sampling Techniques
Neural Processing Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Low complexity MLP-based radar detector: influence of the training algorithm and the MLP size
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Neural network detectors for composite hypothesis tests
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
NN-Based detector for known targets in coherent weibull clutter
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Decentralized detection in sensor networks
IEEE Transactions on Signal Processing
Neural networks for signal detection in non-Gaussian noise
IEEE Transactions on Signal Processing
A Neyman-Pearson approach to statistical learning
IEEE Transactions on Information Theory
Neyman-pearson detection of gauss-Markov signals in noise: closed-form error exponentand properties
IEEE Transactions on Information Theory
Any reasonable cost function can be used for a posteriori probability approximation
IEEE Transactions on Neural Networks
EURASIP Journal on Advances in Signal Processing - Special issue on advances in signal processing for maritime applications
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Hi-index | 35.68 |
A study of the possibility of approximating the Neyman-Pearson detector using supervised learning machines is presented. Two error functions are considered for training: the sum-of-squares error and the Minkowski error with R = 1. The study is based on the calculation of the function the learning machine approximates to during training, and the application of a sufficient condition previously formulated. Some experiments about signal detection using neural networks are also presented to test the validity of the study. Theoretical and experimental results demonstrate, on one hand, that only the sum-of-squares error is suitable to approximate the Neyman-Pearson detector and, on the other hand, that the Minkowski error with R = 1 is suitable to approximate the minimum probability of error classifier.