Browsing by Author "Civek, Burak Cevat"

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Open Access
Big data signal processing using boosted RLS algorithm
(IEEE, 2016) Civek, Burak Cevat; Kari, Dariush; Delibalta, İ.; Kozat, Süleyman Serdar
We propose an efficient method for the high dimensional data regression. To this end, we use a least mean squares (LMS) filter followed by a recursive least squares (RLS) filter and combine them via boosting notion extensively used in machine learning literature. Moreover, we provide a novel approach where the RLS filter is updated randomly in order to reduce the computational complexity while not giving up more on the performance. In the proposed algorithm, after the LMS filter produces an estimate, depending on the error made on this step, the algorithm decides whether or not updating the RLS filter. Since we avoid updating the RLS filter for all data sequence, the computational complexity is significantly reduced. Error performance and the computation time of our algorithm is demonstrated for a highly realistic scenario.
Open Access
Nonlinear regression using second order methods
(IEEE, 2016) Civek, Burak Cevat; Delibalta, İ.; Kozat, Süleyman Serdar
We present a highly efficient algorithm for the online nonlinear regression problem. We process only the currently available data and do not reuse it, hence, there is no need for storage. For the nonlinear regression, we use piecewise linear modeling, where the regression space is partitioned into several regions and a linear model is fit to each region. As the first time in the literature, we use second order methods, e.g. Newton-Raphson Methods, and adaptively train both the region boundaries and the corresponding linear models. Therefore, we overcome the well known overfitting and underfitting problems. The proposed algorithm provides a substantial improvement in the performance compared to the state of the art.
Open Access
Piecewise linear regression based on adaptive tree structure using second order methods
(IEEE, 2016) Civek, Burak Cevat; Delibalta, İ.; Kozat, Süleyman Serdar
We introduce a highly efficient online nonlinear regression algorithm. We process the data in a truly online manner such that no storage is needed, i.e., the data is discarded after used. For nonlinear modeling we use a hierarchical piecewise linear approach based on the notion of decision trees, where the regressor space is adaptively partitioned based directly on the performance. As the first time in the literature, we learn both the piecewise linear partitioning of the regressor space as well as the linear models in each region using highly effective second order methods, i.e., Newton-Raphson Methods. Hence, we avoid the well known over fitting issues and achieve substantial performance compared to the state of the art. We demonstrate our gains over the well known benchmark data sets and provide performance results in an individual sequence manner guaranteed to hold without any statistical assumptions.
Open Access
Sequential regression techniques with second order methods
(2017-07) Civek, Burak Cevat
Sequential regression problem is one of the widely investigated topics in the machine learning and the signal processing literatures. In order to adequately model the underlying structure of the real life data sequences, many regression methods employ nonlinear modeling approaches. In this context, in the rst chapter, we introduce highly e cient sequential nonlinear regression algorithms that are suitable for real life applications. We process the data in a truly online manner such that no storage is needed. For nonlinear modeling we use a hierarchical piecewise linear approach based on the notion of decision trees where the space of the regressor vectors is adaptively partitioned. As the rst time in the literature, we learn both the piecewise linear partitioning of the regressor space as well as the linear models in each region using highly e ective second order methods, i.e., Newton- Raphson Methods. Hence, we avoid the well-known over tting issues by using piecewise linear models and achieve substantial performance compared to the state of the art. In the second chapter, we investigate the problem of sequential prediction for real life big data applications. The second order Newton-Raphson methods asymptotically achieve the performance of the \best" possible predictor much faster compared to the rst order algorithms. However, their usage in real life big data applications is prohibited because of the extremely high computational needs. To this end, in order to enjoy the outstanding performance of the second order methods, we introduce a highly e cient implementation where the computational complexity is reduced from quadratic to linear scale. For both chapters, we demonstrate our gains over the well-known benchmark and real life data sets and provide performance results in an individual sequence manner guaranteed to hold without any statistical assumptions.