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Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling

dc.contributor.authorMa, Zen_US
dc.contributor.authorLai, Yen_US
dc.contributor.authorKleijn, WBen_US
dc.contributor.authorSONG, Yen_US
dc.contributor.authorWang, Len_US
dc.contributor.authorGuo, Jen_US
dc.date.accessioned2018-07-13T14:50:00Z
dc.date.available2018-05-31en_US
dc.date.issued2018-07-02en_US
dc.date.submitted2018-05-31T16:10:58.438Z
dc.identifier.issn2162-237Xen_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/42125
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.titleVariational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modelingen_US
dc.typeArticle
dc.rights.holder© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.identifier.doi10.1109/TNNLS.2018.2844399en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
dcterms.dateAccepted2018-05-31en_US


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