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A new learning paradigm for random vector functional-link network: RVFL+
Zhang,Peng Bo; Yang,Zhi Xin
Source PublicationNeural Networks

In school, a teacher plays an important role in various classroom teaching patterns. Likewise to this human learning activity, the learning using privileged information (LUPI) paradigm provides additional information generated by the teacher to ’teach’ learning models during the training stage. Therefore, this novel learning paradigm is a typical Teacher–Student Interaction mechanism. This paper is the first to present a random vector functional link (RVFL) network based on the LUPI paradigm, called RVFL+. The novel RVFL+ incorporates the LUPI paradigm that can leverage additional source of information into the RVFL, which offers an alternative way to train the RVFL. Rather than simply combining two existing approaches, the newly-derived RVFL+ fills the gap between classical randomized neural networks and the newfashioned LUPI paradigm. Moreover, the proposed RVFL+ can perform in conjunction with the kernel trick for highly complicated nonlinear feature learning, termed KRVFL+. Furthermore, the statistical property of the proposed RVFL+ is investigated, and the authors present a sharp and high-quality generalization error bound based on the Rademacher complexity. Competitive experimental results on 14 real-world datasets illustrate the great effectiveness and efficiency of the novel RVFL+ and KRVFL+, which can achieve better generalization performance than state-of-the-art methods.

KeywordKrvfl++ Learning Using Privileged Information Random Vector Functional Link Networks Rvfl++ Svm++ The Rademacher Complexity
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000505021700006
Scopus ID2-s2.0-85074151674
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Cited Times [WOS]:30   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorYang,Zhi Xin
AffiliationState Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering,Faculty of Science and Technology,University of Macau,Macau SAR,999078,China
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Zhang,Peng Bo,Yang,Zhi Xin. A new learning paradigm for random vector functional-link network: RVFL+[J]. Neural Networks,2020,122:94-105.
APA Zhang,Peng Bo,&Yang,Zhi Xin.(2020).A new learning paradigm for random vector functional-link network: RVFL+.Neural Networks,122,94-105.
MLA Zhang,Peng Bo,et al."A new learning paradigm for random vector functional-link network: RVFL+".Neural Networks 122(2020):94-105.
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