Now showing items 1-5 of 5

    • Batch learning of disjoint feature intervals 

      Akkuş, Aynur (Bilkent University, 1996)
      This thesis presents several learning algorithms for multi-concept descriptions in the form of disjoint feature intervals, called Feature Interval Learning algorithms (FIL). These algorithms are batch supervised inductive ...
    • Benefit maximizing classification using feature intervals 

      İkizler, Nazlı (Bilkent University, 2002)
      For a long time, classification algorithms have focused on minimizing the quantity of prediction errors by assuming that each possible error has identical consequences. However, in many real-world situations, this ...
    • Big-data streaming applications scheduling based on staged multi-armed bandits 

      Kanoun, K.; Tekin, C.; Atienza, D.; Van Der Schaar, M. (Institute of Electrical and Electronics Engineers, 2016)
      Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to ...
    • Classification with overlapping feature intervals 

      Koç, Hakime Ünsal (Bilkent University, 1995)
      This thesis presents a new form of exemplar-based learning method, based on overlapping feature intervals. Classification with Overlapping Feature Intervals (COFI) is the particular implementation of this technique. In ...
    • Statistical analysis methods for the fMRI data 

      Behroozi, M.; Daliri, M.R.; Boyaci H. (2011)
      Functional magnetic resonance imaging (fMRI) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. The technique has become a ubiquitous tool in basic, clinical ...