Advances in Neyman-Pearson Neural Detectors Design

Authors:
Diego Andina;Santiago Torres-Alegre;Antonio Vega-Corona;Antonio Álvarez-Vellisco
Affiliations:
Departamento de Señales, Sistemas y Radiocomunicaciones, E.T.S.I. Telecomunicación, Universidad Politécnica de Madrid, Spain;Departamento de Señales, Sistemas y Radiocomunicaciones, E.T.S.I. Telecomunicación, Universidad Politécnica de Madrid, Spain;Departamento de Señales, Sistemas y Radiocomunicaciones, E.T.S.I. Telecomunicación, Universidad Politécnica de Madrid, Spain;Departamento de Ingeniería de Circuitos y Sistemas, E.U.I.T. Telecomunicación, Universidad Politécnica de Madrid, Spain
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

Citing 3
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Abstract

This chapter is dedicated to scope of the application of Importance Sampling Techniques to the design phase of Neyman-Pearson Neural Detectors. This phase usually requires the application of Monte- Carlo trials in order to estimate some performance parameters. The classical Monte-Carlo method is suitable to estimate high event probabilities but not suitable to estimate very low event probabilities (say, 10-4or less). For estimations of very low false-alarm probabilities (or error probabilities), a modified Monte-Carlo technique, so-called Importance Sampling (IS) technique, is then considered.