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dc.contributor.authorZhu, L.
dc.contributor.authorBan, T.
dc.contributor.authorIkeda, K.
dc.contributor.authorPang, P.
dc.contributor.authorSarrafzadeh, Hossein
dc.contributor.editor-
dc.date.accessioned2017-07-04T23:55:12Z
dc.date.available2017-07-04T23:55:12Z
dc.date.issued2016-12
dc.identifier.urihttps://hdl.handle.net/10652/3834
dc.description.abstractWeighted linear proximal support vector machine (wLPSVM) is known as an efficient binary classification algorithm with good accuracy and class-imbalance robustness. In this work, original batch wLPSVM is facilitated with distributed incremental learning capability, which allows simultaneously learning from multiple streaming data sources that are geographically distributed. In our approach, incremental and distributed learning are solved as a merging problem at the same time. A new wLPSVM expression is derived. In the new expression, knowledge from samples are presented as a set of class-wised core matrices, and merging knowledge from two subsets of data can be simply accomplished by matrix addition. With the new expression, we are able to conduct incremental and distributed learning at the same time via merging knowledge from multiple incremental stages and multiple data sources.en_NZ
dc.language.isoenen_NZ
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_NZ
dc.subjectwLPSVMen_NZ
dc.subjectweighted linear proximal support vector machinesen_NZ
dc.subjectclass imbalance learningen_NZ
dc.subjectdistributed incremental learningen_NZ
dc.titleDistributed Incremental wLPSVM Learningen_NZ
dc.typeConference Contribution - Paper in Published Proceedingsen_NZ
dc.date.updated2017-05-10T05:38:05Z
dc.rights.holderAuthorsen_NZ
dc.subject.marsden170203 Knowledge Representation and Machine Learning
dc.identifier.bibliographicCitationZhu, L., Ban, T., Ikeda, K., Pang, P., & Sarrafzadeh, A. (2016, December). Distributed Incremental wLPSVM Learning. - (Ed.), IEEE Symposium Series on Computational Intelligence (SSCI) (paper 45)en_NZ
unitec.publication.titleIEEE Symposium Series on Computational Intelligence (SSCI)en_NZ
unitec.conference.titleIEEE Symposium Series on Computational Intelligence (SSCI)en_NZ
unitec.conference.orgInstitute of Electrical and Electronics Engineers (IEEE)en_NZ
unitec.conference.locationAthens, Greeceen_NZ
unitec.conference.sdate2016-12-06
unitec.conference.edate2016-12-09
unitec.peerreviewedyesen_NZ
unitec.identifier.roms59843en_NZ


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