Sequential regression techniques with second order methods
Civek, Burak Cevat
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/33502
Kozat, Süleyman Serdar
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.