NDLI: Threshold Learning from Samples Drawn from the Null Hypothesis for the Generalized Likelihood Ratio CUSUM Test

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Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004.

Threshold Learning from Samples Drawn from the Null Hypothesis for the Generalized Likelihood Ratio CUSUM Test

Content Provider	IEEE Xplore Digital Library
Author	Hory, C. Kokaram, A. Christmas, W.J.
Copyright Year	2005
Description	Author affiliation: EEE Dept., Dublin Univ. (Hory, C.; Kokaram, A.)
Abstract	Although optimality of sequential tests for the detection of a change in the parameter of a model has been widely discussed, the test parameter tuning is still an issue. In this communication, we propose a learning strategy to set the threshold of the GLR CUSUM statistics to take a decision with a desired false alarm probability. Only data before the change point are required to perform the learning process. Extensive simulations are performed to assess the validity of the proposed method. The paper is concluded by opening the path to a new approach to multi-modal feature based event detection for video parsing
Starting Page	111
Ending Page	116
File Size	469979
Page Count	6
File Format	PDF
ISBN	0780395174
DOI	10.1109/MLSP.2005.1532884
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Sequential analysis Hidden Markov models Event detection Streaming media Educational institutions Probability Performance evaluation Speech processing Signal processing
Content Type	Text
Resource Type	Article

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