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DC Field | Value | Language |
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dc.contributor.author | Lai, Y | - |
dc.contributor.author | Guan, W | - |
dc.contributor.author | Luo, L | - |
dc.contributor.author | Guo, Y | - |
dc.contributor.author | Song, H | - |
dc.contributor.author | Meng, H | - |
dc.date.accessioned | 2022-11-05T12:57:11Z | - |
dc.date.available | 2022-11-05T12:57:11Z | - |
dc.date.issued | 2022-10-25 | - |
dc.identifier | ORCID iD: Yuping Lai https://orcid.org/0000-0002-3797-1228 | - |
dc.identifier | ORCID iD: Wenbo Guan https://orcid.org/0000-0002-4645-6121 | - |
dc.identifier | ORCID iD: Lijuan Luo https://orcid.org/0000-0002-3702-372X | - |
dc.identifier | ORCID iD: Heping Song https://orcid.org/0000-0002-8583-2804 | - |
dc.identifier | ORCID iD: Hongying Meng https://orcid.org/0000-0002-8836-1382 | - |
dc.identifier.citation | Lai, Y..et al. (2022) 'Bayesian Estimation of Inverted Beta Mixture Models With Extended Stochastic Variational Inference for Positive Vector Classification', IEEE Transactions on Neural Networks and Learning Systems, 0 (early access), pp. 1 - 15. doi: 10.1109/tnnls.2022.3213518 | en_US |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25452 | - |
dc.description.sponsorship | 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62272051, 62172193 and 72101157); 10.13039/501100012226-Fundamental Research Funds for the Central Universities (Grant Number: 2022RC16); Research and Development Program of Beijing Municipal Education Commission (Grant Number: KM201910009014). | en_US |
dc.format.extent | 1 - 15 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2022 Institute of Electrical and Electronics Engineers (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. See: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.subject | extended stochastic variational inference | en_US |
dc.subject | mixture models | en_US |
dc.subject | Bayesian estimation | en_US |
dc.subject | text categrization | en_US |
dc.subject | network traffiic classification | en_US |
dc.subject | misuse intrusion detecton | en_US |
dc.title | Bayesian Estimation of Inverted Beta Mixture Models With Extended Stochastic Variational Inference for Positive Vector Classification | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/tnnls.2022.3213518 | - |
dc.relation.isPartOf | IEEE Transactions on Neural Networks and Learning Systems | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 2162-2388 | - |
dc.rights.holder | IEEE | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2022 Institute of Electrical and Electronics Engineers (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. See: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | 23.14 MB | Adobe PDF | View/Open |
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