Online nonlinear modeling via self-organizing trees

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dc.citation.epage222en_US
dc.citation.spage201en_US
dc.contributor.authorVanlı, Nuri Denizcanen_US
dc.contributor.authorKozat, Süleyman Serdaren_US
dc.contributor.editorComminiello, D.
dc.contributor.editorPríncipe, J. C.
dc.date.accessioned2019-05-28T06:42:44Z
dc.date.available2019-05-28T06:42:44Z
dc.date.issued2018en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe study online supervised learning and introduce regression and classification algorithms based on self-organizing trees (SOTs), which adaptively partition the feature space into small regions and combine simple local learners defined in these regions. The proposed algorithms sequentially minimize the cumulative loss by learning both the partitioning of the feature space and the parameters of the local learners defined in each region. The output of the algorithm at each time instance is constructed by combining the outputs of a doubly exponential number (in the depth of the SOT) of different predictors defined on this tree with reduced computational and storage complexity. The introduced methods are generic, such that they can incorporate different tree construction methods from the ones presented in this chapter. We present a comprehensive experimental study under stationary and nonstationary environments using benchmark datasets and illustrate remarkable performance improvements with respect to the state-of-the-art methods in the literature.en_US
dc.identifier.doi10.1016/B978-0-12-812976-0.00012-9en_US
dc.identifier.eisbn9780128129777
dc.identifier.isbn9780128129760
dc.identifier.urihttp://hdl.handle.net/11693/51918
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.ispartofAdaptive learning methods for nonlinear system modelingen_US
dc.relation.isversionofhttps://doi.org/10.1016/B978-0-12-812976-0.00012-9en_US
dc.titleOnline nonlinear modeling via self-organizing treesen_US
dc.typeBook Chapteren_US
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