View source for Publications:A Kernel based multi-resolution time series analysis for screening deficiencies in paper production

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{{PublicationSetupTemplate|Author=Marcus Ejnarsson, Carl Magnus Nilsson, Antanas Verikas
|PID=239221
|Name=Ejnarsson, Marcus [marejnx] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941]);Nilsson, Carl Magnus [cmn] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941]);Verikas, Antanas [av] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941])
|Title=A Kernel based multi-resolution time series analysis for screening deficiencies in paper production
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=
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|HostPublication=Advances in neural networks - ISNN 2006 : third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006 ; proceedings. III
|Conference=third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006
|StartPage=1111
|EndPage=1116
|Year=2006
|Edition=
|Pages=
|City=Berlin
|Publisher=Springer Berlin/Heidelberg
|Series=Lecture Notes in Computer Science ; Volume 3973/2006
|SeriesISSN=0302-9743
|ISBN=978-3-540-34482-7
|Urls=
|ISRN=
|DOI=http://dx.doi.org/10.1007/11760191
|ISI=000239485300162
|PMID=
|ScopusId=2-s2.0-33745927697
|NBN=urn:nbn:se:hh:diva-2003
|LocalId=2082/2398
|ArchiveNumber=
|Keywords=Discrete transformation;Discrete time;Discrete wavelet;Time analysis;Kernel method;Medical screening;Screening;Neural network
|Categories=Teknik och teknologier (2)
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|Abstract=<p>This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions.</p>
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|CreatedDate=2008-10-06
|PublicationDate=2008-10-06
|LastUpdated=2013-05-24
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239221}}
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