Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
An adaptive multiple kernel method-based support vector machine used for classication
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0002-4107-0991
2013 (engelsk)Inngår i: International Journal of Condition Monitoring, ISSN 0019-6398, E-ISSN 2047-6426, Vol. 3, nr 1, s. 8-15Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Classification is an important technique used for condition monitoring. Extensive research has been carried out on classification and numerous techniques have been developed. The support vector machine (SVM) is one of these techniques; it has excellent classification capacity and is widely used. The effectiveness of the SVM depends on the selection of the kernel function, so to maximise performance this paper proposes using an adaptive multiple kernel SVM (AMK-SVM). Using AMK, many heterogeneous features, such as continuous, categorical, logical etc, can be merged. Instead of predefining the parameters of kernel functions as with other multiple kernel SVMs, this method can adapt its parameters to data automatically through kernel alignment. The paper offers two numerical examples: one with benchmarking data to test the feasibility and performance of the approach (for this case a two-layer neural network and two single kernel SVMs are applied to the same datasets to compare their performance with the AMK-SVM); the other example uses the AMK-SVM to discriminate a healthy bearing from a defective bearing

sted, utgiver, år, opplag, sider
2013. Vol. 3, nr 1, s. 8-15
HSV kategori
Forskningsprogram
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-9182DOI: 10.1784/204764213806173367Lokal ID: 7bedcc20-3421-4f03-9d51-15a7b15b06f6OAI: oai:DiVA.org:ltu-9182DiVA, id: diva2:982120
Merknad
Validerad; 2013; 20131202 (yuafuq)Tilgjengelig fra: 2016-09-29 Laget: 2016-09-29 Sist oppdatert: 2017-11-24bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Personposter BETA

Fuqing, YuanKumar, UdayGalar, Diego

Søk i DiVA

Av forfatter/redaktør
Fuqing, YuanKumar, UdayGalar, Diego
Av organisasjonen
I samme tidsskrift
International Journal of Condition Monitoring

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 56 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf