Now showing items 1-8 of 8

    • 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 ...
    • Adaptive ensemble learning with confidence bounds for personalized diagnosis 

      Tekin, Cem; Yoon, J.; Van Der Schaar, M. (AAAI Press, 2016)
      With the advances in the field of medical informatics, automated clinical decision support systems are becoming the de facto standard in personalized diagnosis. In order to establish high accuracy and confidence in ...
    • 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 ...
    • 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 ...
    • GOOWE-ML: a novel online stacked ensemble for multi-label classification in data streams 

      Büyükçakır, Alican (Bilkent University, 2019-07)
      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 ...
    • A novel online stacked ensemble for multi-label stream classification 

      Büyükçakır, Alican; Bonab, H.; Can, Fazlı (ACM, 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 ...
    • On-the-fly ensemble classifier pruning in evolving data streams 

      Elbaşı, Sanem (Bilkent University, 2019-09)
      Ensemble pruning is the process of selecting a subset of component classifiers from an ensemble which performs at least as well as the original ensemble while reducing storage and computational costs. Ensemble pruning ...
    • Online boosting algorithm for regression with additive and multiplicative updates 

      Mirza, Ali H. (IEEE, 2018-05)
      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 ...