Importance Sampling Techniques in Neural Detector Training
Abstract
Importance Sampling is a modified Monte Carlo technique applied to the estimation of rare event probabilities (very low probabilities). In this paper, we propose and develop the use of Importance Sampling (IS) techniques in neural network training, for applications to detection in communication systems. Some key topics are introduced, such as modifications of the error probability objective function, optimal and suboptimal IS probability density functions (biasing density functions), and experimental results of training with a genetic algorithm. Also, it is shown that the genetic algorithm with the IS technique attains quasi-optimum training in the sense of minimum error probability (or minimum misclassification probability).
Cite
Text
Sanz-González and Andina. "Importance Sampling Techniques in Neural Detector Training." European Conference on Machine Learning, 2001. doi:10.1007/3-540-44795-4_37Markdown
[Sanz-González and Andina. "Importance Sampling Techniques in Neural Detector Training." European Conference on Machine Learning, 2001.](https://mlanthology.org/ecmlpkdd/2001/sanzgonzalez2001ecml-importance/) doi:10.1007/3-540-44795-4_37BibTeX
@inproceedings{sanzgonzalez2001ecml-importance,
title = {{Importance Sampling Techniques in Neural Detector Training}},
author = {Sanz-González, José L. and Andina, Diego},
booktitle = {European Conference on Machine Learning},
year = {2001},
pages = {431-441},
doi = {10.1007/3-540-44795-4_37},
url = {https://mlanthology.org/ecmlpkdd/2001/sanzgonzalez2001ecml-importance/}
}