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dc.contributor.authorKOKARAM, ANIL CHRISTOPHER
dc.date.accessioned2008-07-30T16:09:17Z
dc.date.available2008-07-30T16:09:17Z
dc.date.created28-30 Sept.en
dc.date.issued2005
dc.date.submitted2005en
dc.identifier.citationC. Hory, A. Kokaram and W. J. Christmas 'Threshold learning from samples drawn from the null hypothesis for the generalized likelihood ratio cusum test' in proceedings of IEEE International Workshop on Machine Learning for Signal Processing, Mystic, Connecticut, USA., 28-30 Sept., 2005, pp 111 - 116.en
dc.identifier.issn27120
dc.identifier.otherY
dc.identifier.urihttp://hdl.handle.net/2262/19819
dc.descriptionPUBLISHEDen
dc.description.abstractAlthough 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 parsingen
dc.description.sponsorshipSFIen
dc.format.extent111en
dc.format.extent116en
dc.format.extent469979 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.rightsYen
dc.subjectElectronic & Electrical Engineeringen
dc.titleThreshold learning from samples drawn from the null hypothesis for the generalized likelihood ratio cusum testen
dc.title.alternativeIEEE International Workshop on Machine Learning for Signal Processingen
dc.typeConference Paperen
dc.contributor.sponsorScience Foundation Ireland
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/akokaram
dc.identifier.rssurihttp://ieeexplore.ieee.org/iel5/10270/32703/01532884.pdf?tp=&isnumber=32703&arnumber=1532884&punumber=10270
dc.identifier.rssurihttp://www.sigmedia.tv


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