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dc.contributor.advisorKozat, Süleyman Serdar
dc.contributor.authorKhan, Farhan
dc.date.accessioned2017-12-28T13:51:24Z
dc.date.available2017-12-28T13:51:24Z
dc.date.copyright2017-12
dc.date.issued2017-12
dc.date.submitted2017-12-28
dc.identifier.urihttp://hdl.handle.net/11693/35712
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Ph.D.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2017.en_US
dc.descriptionIncludes bibliographical references (leaves 112-128).en_US
dc.description.abstractWe investigate online nonlinear learning for several real life, adaptive signal processing and machine learning applications involving big data, and introduce algorithms that are both e cient and e ective. We present novel solutions for learning from the data that is generated at high speed and/or have big dimensions in a non-stationary environment, and needs to be processed on the y. We speci cally focus on investigating the problems arising from adverse real life conditions in a big data perspective. We propose online algorithms that are robust against the non-stationarities and corruptions in the data. We emphasize that our proposed algorithms are universally applicable to several real life applications regardless of the complexities involving high dimensionality, time varying statistics, data structures and abrupt changes. To this end, we introduce a highly robust hierarchical trees algorithm for online nonlinear learning in a high dimensional setting where the data lies on a time varying manifold. We escape the curse of dimensionality by tracking the subspace of the underlying manifold and use the projections of the original high dimensional regressor space onto the underlying manifold as the modi ed regressor vectors for modeling of the nonlinear system. By using the proposed algorithm, we reduce the computational complexity to the order of the depth of the tree and the memory requirement to only linear in the intrinsic dimension of the manifold. We demonstrate the signi cant performance gains in terms of mean square error over the other state of the art techniques through simulated as well as real data. We then consider real life applications of online nonlinear learning modeling, such as network intrusions detection, customers' churn analysis and channel estimation for underwater acoustic communication. We propose sequential and online learning methods that achieve signi cant performance in terms of detection accuracy, compared to the state-of-the-art techniques. We speci cally introduce structured and deep learning methods to develop robust learning algorithms. Furthermore, we improve the performance of our proposed online nonlinear learning models by introducing mixture-of-experts methods and the concept of boosting. The proposed algorithms achieve signi cant performance gain over the state-ofthe- art methods with signi cantly reduced computational complexity and storage requirements in real life conditions.en_US
dc.description.statementofresponsibilityby Farhan Khan.en_US
dc.format.extentxv, 129 leaves : charts (some color) ; 30 cmen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectOnline learningen_US
dc.subjectBig dataen_US
dc.subjectBoostingen_US
dc.subjectChannel estimationen_US
dc.subjectSequential data processingen_US
dc.subjectİntrusion detectionen_US
dc.subjectUnderwater acousticsen_US
dc.subjectChannel estimationen_US
dc.subjectLanguage modelen_US
dc.subjectTree based methodsen_US
dc.subjectLogistic regressionen_US
dc.subjectDeterministic analysisen_US
dc.subjectNon-Stationarityen_US
dc.subjectCurse of dimensionalityen_US
dc.subjectStream processingen_US
dc.subjectTime seriesen_US
dc.titleOnline nonlinear modeling for big data applicationsen_US
dc.title.alternativeBüyük veri uygulamaları için online non lineer olmayan modellemeen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh.D.en_US
dc.identifier.itemidB157334


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