Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-03983-6_34
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dc.titleEquivalent relationship of feedforward neural networks and real-time face detection system
dc.contributor.authorGe, S.S.
dc.contributor.authorPan, Y.
dc.contributor.authorZhang, Q.
dc.contributor.authorChen, L.
dc.date.accessioned2014-04-24T08:34:56Z
dc.date.available2014-04-24T08:34:56Z
dc.date.issued2009
dc.identifier.citationGe, S.S., Pan, Y., Zhang, Q., Chen, L. (2009). Equivalent relationship of feedforward neural networks and real-time face detection system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5744 LNCS : 301-310. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-03983-6_34
dc.identifier.isbn3642039820
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/51157
dc.description.abstractIn this paper, we mainly investigate a fast algorithm, Extreme Learning Machine (ELM), on its equivalent relationship, approximation capability and real-time face detection application. Firstly, an equivalent relationship is presented for neural networks without orthonormalization (ELM) and orthonormal neural networks. Secondly, based on the equivalent relationship and the universal approximation of orthonormal neural networks, we successfully prove that neural networks with ELM have the property of universal approximation, and adjustable parameters of hidden neurons and orthonormal transformation are not necessary. Finally, based on the fast learning characteristic of ELM, we successfully combine ELM with AdaBoost algorithm of Viola-Jones in face detection applications such that the whole system not only retains a real-time learning speed, but also possesses high face detection accuracy. © 2009 Springer.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-03983-6_34
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentINTERACTIVE & DIGITAL MEDIA INSTITUTE
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/978-3-642-03983-6_34
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5744 LNCS
dc.description.page301-310
dc.identifier.isiutNOT_IN_WOS
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