Difference between revisions of "Publications:A Kernel based multi-resolution time series analysis for screening deficiencies in paper production"
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| − | |Name=Ejnarsson, Marcus | + | |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) (0000-0003-2185-8973) (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 | |Title=A Kernel based multi-resolution time series analysis for screening deficiencies in paper production | ||
|PublicationType=Conference Paper | |PublicationType=Conference Paper | ||
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|CreatedDate=2008-10-06 | |CreatedDate=2008-10-06 | ||
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Latest revision as of 21:40, 30 September 2016
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| Title | A Kernel based multi-resolution time series analysis for screening deficiencies in paper production |
|---|---|
| Author | |
| Year | 2006 |
| PublicationType | Conference Paper |
| Journal | |
| 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 |
| DOI | http://dx.doi.org/10.1007/11760191 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239221 |
| Abstract | 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. |
