Now showing items 1-5 of 5

    • Adaptive and efficient nonlinear channel equalization for underwater acoustic communication 

      Kari, D.; Vanli, N. D.; Kozat, S. S. (Elsevier B.V., 2017)
      We investigate underwater acoustic (UWA) channel equalization and introduce hierarchical and adaptive nonlinear (piecewise linear) channel equalization algorithms that are highly efficient and provide significantly improved ...
    • Boosted LMS-Based Piecewise Linear Adaptive Filters 

      Kari, D.; Marivani, I.; Delibalta, I.; Kozat, S.S. (European Signal Processing Conference, EUSIPCO, 2016)
      We introduce the boosting notion extensively used in different machine learning applications to adaptive signal processing literature and implement several different adaptive filtering algorithms. In this framework, we ...
    • Competitive and online piecewise linear classification 

      Ozkan H.; Donmez, M.A.; Pelvan O.S.; Akman, A.; Kozat, S.S. (2013)
      In this paper, we study the binary classification problem in machine learning and introduce a novel classification algorithm based on the 'Context Tree Weighting Method'. The introduced algorithm incrementally learns a ...
    • Highly efficient nonlinear regression for big data with lexicographical splitting 

      Neyshabouri, M. M.; Demir, O.; Delibalta, I.; Kozat, S. S. (Springer London, 2017)
      This paper considers the problem of online piecewise linear regression for big data applications. We introduce an algorithm, which sequentially achieves the performance of the best piecewise linear (affine) model with ...
    • Linear MMSE-optimal turbo equalization using context trees 

      Kim, K.; Kalantarova, N.; Kozat, S. S.; Singer, A. C. (IEEE, 2013)
      Formulations of the turbo equalization approach to iterative equalization and decoding vary greatly when channel knowledge is either partially or completely unknown. Maximum aposteriori probability (MAP) and minimum ...