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dc.contributor.authorQin, Yuping
dc.contributor.authorKarimi, Hamid Reza
dc.contributor.authorLi, Dan
dc.contributor.authorLun, Shuxian
dc.contributor.authorZhang, Aihua
dc.date.accessioned2014-12-18T08:29:16Z
dc.date.available2014-12-18T08:29:16Z
dc.date.issued2014
dc.identifier.citationQin, Y., Karimi, H. R., Li, D., Lun, S., & Zhang, A. (2014). A mahalanobis hyperellipsoidal learning machine class incremental learning algorithm. Abstract and Applied Analysis, 2014, 1-5. doi: 10.1155/2014/894246nb_NO
dc.identifier.issn1085-3375
dc.identifier.urihttp://hdl.handle.net/11250/227712
dc.descriptionPublished version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/894246 Open Accessnb_NO
dc.description.abstractA Mahalanobis hyperellipsoidal learning machine class incremental learning algorithm is proposed. To each class sample, the hyperellipsoidal that encloses as many as possible and pushes the outlier samples away is trained in the feature space. In the process of incremental learning, only one subclassifier is trained with the new class samples. The old models of the classifier are not influenced and can be reused. In the process of classification, considering the information of sample's distribution in the feature space, the Mahalanobis distances from the sample mapping to the center of each hyperellipsoidal are used to decide the classified sample class. The experimental results show that the proposed method has higher classification precision and classification speed.nb_NO
dc.language.isoengnb_NO
dc.publisherHindawi Publishing Corporationnb_NO
dc.titleA mahalanobis hyperellipsoidal learning machine class incremental learning algorithmnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.subject.nsiVDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411nb_NO
dc.source.pagenumber1-5nb_NO
dc.source.journalAbstract and Applied Analysisnb_NO
dc.identifier.doi10.1155/2014/894246


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