dc.contributor.advisor | Kozat, Süleyman Serdar | |
dc.contributor.author | Khan, Farhan | |
dc.date.accessioned | 2017-12-28T13:51:24Z | |
dc.date.available | 2017-12-28T13:51:24Z | |
dc.date.copyright | 2017-12 | |
dc.date.issued | 2017-12 | |
dc.date.submitted | 2017-12-28 | |
dc.identifier.uri | http://hdl.handle.net/11693/35712 | |
dc.description | Cataloged from PDF version of article. | en_US |
dc.description | Thesis (Ph.D.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2017. | en_US |
dc.description | Includes bibliographical references (leaves 112-128). | en_US |
dc.description.abstract | We 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.statementofresponsibility | by Farhan Khan. | en_US |
dc.format.extent | xv, 129 leaves : charts (some color) ; 30 cm | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Online learning | en_US |
dc.subject | Big data | en_US |
dc.subject | Boosting | en_US |
dc.subject | Channel estimation | en_US |
dc.subject | Sequential data processing | en_US |
dc.subject | İntrusion detection | en_US |
dc.subject | Underwater acoustics | en_US |
dc.subject | Channel estimation | en_US |
dc.subject | Language model | en_US |
dc.subject | Tree based methods | en_US |
dc.subject | Logistic regression | en_US |
dc.subject | Deterministic analysis | en_US |
dc.subject | Non-Stationarity | en_US |
dc.subject | Curse of dimensionality | en_US |
dc.subject | Stream processing | en_US |
dc.subject | Time series | en_US |
dc.title | Online nonlinear modeling for big data applications | en_US |
dc.title.alternative | Büyük veri uygulamaları için online non lineer olmayan modelleme | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | Ph.D. | en_US |
dc.identifier.itemid | B157334 | |