Sub-Optimum Signal Linear Detector Using Wavelets and Support Vector Machines

05/20/2005 ∙ by Jaime Gomez, et al. ∙ 0 ∙ share

The problem of known signal detection in Additive White Gaussian Noise is considered. In previous work, a new detection scheme was introduced by the authors, and it was demonstrated that optimum performance cannot be reached in a real implementation. In this paper we analyse Support Vector Machines (SVM) as an alternative, evaluating the results in terms of Probability of detection curves for a fixed Probability of false alarm.

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Upgrading Pulse Detection with Time Shift Properties Using Wavelets and Support Vector Machines

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Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach

Support vector machines (SVMs) are an important tool in modern data anal...
05/12/2017 ∙ by Hien D. Nguyen, et al. ∙ 0 ∙ share

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Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines

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Sub-Optimum Signal Linear Detector Using Wavelets and Support Vector Machines

Authors

Qualitative Robustness of Support Vector Machines

Upgrading Pulse Detection with Time Shift Properties Using Wavelets and Support Vector Machines

Wavelet Time Shift Properties Integration with Support Vector Machines

On detection of Gaussian stochastic sequences

Early warning in egg production curves from commercial hens: A SVM approach

Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach

Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines

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