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

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dc.creatorGomez, Jaime
dc.creatorMelgar, Ignacio
dc.creatorSeijas, Juan
dc.creatorAndina, Diego
dc.date2005-05-20
dc.date.accessioned2026-07-25T16:54:00Z
dc.descriptionThe 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.
dc.description6 pages
dc.identifierhttps://arxiv.org/abs/cs/0505051
dc.identifierhttp://arxiv.org/abs/cs/0505051
dc.identifierWSEAS Transactions on Communications, ISSN 1109-2742, issue 4, vol 2, p426-431, October-2003
dc.identifier.urihttps://dspace.dare.co.zw/handle/123456789/43979
dc.subjectInformation Retrieval
dc.subjectNeural and Evolutionary Computing
dc.titleSub-Optimum Signal Linear Detector Using Wavelets and Support Vector Machines
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