Now showing items 1-6 of 6

    • Canlı hücre bölütlemesi için gözeticili öğrenme modeli 

      Koyuncu, Can Fahrettin; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, Çiğdem (IEEE Computer Society, 2014-04)
      Automated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the ...
    • 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 ...
    • Non-uniformly sampled sequential data processing 

      Şahin, Safa Onur (Bilkent University, 2019-09)
      We study classification and regression for variable length sequential data, which is either non-uniformly sampled or contains missing samples. In most sequential data processing studies, one considers data sequence is ...
    • 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 ...
    • Online learning under adverse settings 

      Özkan, Hüseyin (Bilkent University, 2015-05)
      We present novel solutions for contemporary real life applications that generate data at unforeseen rates in unpredictable forms including non-stationarity, corruptions, missing/mixed attributes and high dimensionality. ...
    • Weakly supervised object localization with multi-fold multiple instance learning 

      Cinbis, R. G.; Verbeek, J.; Schmid, C. (IEEE Computer Society, 2017)
      Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakly ...