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An adaptive multiple kernel method-based support vector machine used for classication
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-8111-6918
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-4107-0991
2013 (English)In: International Journal of Condition Monitoring, E-ISSN 2047-6426, Vol. 3, no 1, p. 8-15Article in journal (Refereed) 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

Place, publisher, year, edition, pages
2013. Vol. 3, no 1, p. 8-15
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-9182DOI: 10.1784/204764213806173367Local ID: 7bedcc20-3421-4f03-9d51-15a7b15b06f6OAI: oai:DiVA.org:ltu-9182DiVA, id: diva2:982120
Note

Validerad; 2013; 20131202 (yuafuq)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2022-07-14Bibliographically approved

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Fuqing, YuanKumar, UdayGalar, Diego

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