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dc.contributor.authorVrba, Jan
dc.contributor.authorMareš, Jan
dc.contributor.editorPinker, Jiří
dc.date.accessioned2020-11-05T14:39:34Z
dc.date.available2020-11-05T14:39:34Z
dc.date.issued2020
dc.identifier.citation2020 International Conference on Applied Electronics: Pilsen, 8th – 9h September 2020, Czech Republic.en
dc.identifier.isbn978-80-261-0891-7 (Print)
dc.identifier.isbn978-80-261-0892-4 (Online)
dc.identifier.issn1803-7232 (Print)
dc.identifier.issn1805-9597 (Online)
dc.identifier.urihttp://hdl.handle.net/11025/39932
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 novinkycs
dc.subjectdetekce změny trenducs
dc.titleROC Analysis of Extreme Seeking Entropyfor Trend Change Detectionen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper is dedicated to the evaluation ofthe ROC curve of recently introduced Extreme SeekingEntropy algorithm. The ROC curve is evaluated for atrend change in the signal that contains additive Gaussiannoise. The resulting ROC curve of the Extreme SeekingEntropy algorithm is compared with other adaptivenovelty detection methods, namely Learning Entropyand Error and Learning Based Novelty Detection asthose algorithms are also evaluating the adaptive weightsincrements. The ROC curves are evaluated for multiplenoise variances and area under those ROC curves isestimated.en
dc.subject.translatedsignal processingen
dc.subject.translatedadaptive systemsen
dc.subject.translatedadaptive algorithmsen
dc.subject.translatednovelty detectionen
dc.subject.translatedtrend change detectionen
dc.type.statusPeer-revieweden
Appears in Collections:Applied Electronics 2020
Applied Electronics 2020

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