Now showing items 1-5 of 5

    • Adaptive ensemble learning with confidence bounds 

      Tekin, C.; Yoon, J.; Schaar, M. V. D. (Institute of Electrical and Electronics Engineers Inc., 2017)
      Extracting actionable intelligence from distributed, heterogeneous, correlated, and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of ...
    • Boosted adaptive filters 

      Kari, Dariush (Bilkent University, 2017-07)
      We investigate boosted online regression and propose a novel family of regression algorithms with strong theoretical bounds. In addition, we implement several variants of the proposed generic algorithm. We specifically ...
    • Boosted adaptive filters 

      Kari, D.; Mirza, A. H.; Khan, F.; Ozkan, H.; Kozat, S. S. (Elsevier, 2018)
      We introduce the boosting notion of machine learning to the adaptive signal processing literature. In our framework, we have several adaptive filtering algorithms, i.e., the weak learners, that run in parallel on a common ...
    • A novel online stacked ensemble for multi-label stream classification 

      Büyükçakır, A.; Bonab, H.; Can, F. (Association for Computing Machinery, 2018)
      As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each ...
    • Online boosting algorithm for regression with additive and multiplicative updates 

      Mirza, A. H. (Institute of Electrical and Electronics Engineers, 2018)
      In this paper, we propose a boosted regression algorithm in an online framework. We have a linear combination of the estimated output for each weak learner and weigh each of the estimated output differently by introducing ...