Online boosting algorithm for regression with additive and multiplicative updates

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dc.citation.epage4en_US
dc.citation.spage1en_US
dc.contributor.authorMirza, Ali H.en_US
dc.coverage.spatialIzmir, Turkey
dc.date.accessioned2019-02-21T16:04:58Z
dc.date.available2019-02-21T16:04:58Z
dc.date.issued2018-05en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 2-5 May 2018
dc.descriptionConference name: 26th Signal Processing and Communications Applications Conference (SIU) 2018
dc.description.abstractIn this paper, we propose a boosted regression algorithm in an online framework. We have a linear combination of the estimated output for each weak learner and weigh each of the estimated output differently by introducing ensemble coefficients. We then update the ensemble weight coefficients using both additive and multiplicative updates along with the stochastic gradient updates of the regression weight coefficients. We make the proposed algorithm robust by introducing two critical factors; significance and penalty factor. These two factors play a crucial role in the gradient updates of the regression weight coefficients and in increasing the regression performance. The proposed algorithm is guaranteed to converge in terms of exponentially decaying regret bound in terms of number of weak learners. We then demonstrate the performance of our proposed algorithm on both synthetic as well as real-life data sets.
dc.description.provenanceMade available in DSpace on 2019-02-21T16:04:58Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018en
dc.identifier.doi10.1109/SIU.2018.8404455
dc.identifier.urihttp://hdl.handle.net/11693/50222
dc.language.isoEnglish
dc.publisherIEEE
dc.relation.isversionofhttps://doi.org/10.1109/SIU.2018.8404455
dc.source.title26th IEEE Signal Processing and Communications Applications Conference, SIU 2018en_US
dc.subjectBoosted regressionen_US
dc.subjectBoostingen_US
dc.subjectEnsemble learningen_US
dc.subjectMultiplicative updatesen_US
dc.subjectRegressionen_US
dc.titleOnline boosting algorithm for regression with additive and multiplicative updatesen_US
dc.typeConference Paperen_US

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