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dc.contributor.authorVrba, Jan
dc.contributor.editorPinker, Jiří
dc.date.accessioned2019-10-18T05:34:55Z
dc.date.available2019-10-18T05:34:55Z
dc.date.issued2018
dc.identifier.citation2018 International Conference on Applied Electronics: Pilsen, 11th – 12th September 2018, Czech Republic, 169-172.en
dc.identifier.isbn978–80–261–0721–7
dc.identifier.issn1803–7232
dc.identifier.urihttp://hdl.handle.net/11025/35495
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherZápadočeská univerzita v Plznics
dc.rights© Západočeská univerzita v Plznics
dc.subjectzpracování signálucs
dc.subjectadaptivní systémycs
dc.subjectadaptivní algoritmycs
dc.subjectdetekce změncs
dc.subjectzobecněné rozdělení extrémních hodnotcs
dc.titleAdaptive Novelty Detection with Generalized Extreme Value Distributionen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper introduces the new adaptive novelty detection method. The proposed method is using generalized extreme value distribution to evaluate the absolute value of adaptive system weight increments in time. The detection of novelty is threshold-based and the threshold ζ corresponds to the value of joint probability density function. Performance of the proposed algorithm is shown on artificial data. For comparison also results of Learning Entropy algorithm are shown, as this algorithm also evaluates the increments of adaptive weights.en
dc.subject.translatedsignal processingen
dc.subject.translatedadaptive systemsen
dc.subject.translatedadaptive algorithmsen
dc.subject.translatednovelty detectionen
dc.subject.translatedgeneralized extreme value distributionen
dc.type.statusPeer-revieweden
Appears in Collections:Applied Electronics 2018
Applied Electronics 2018

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