Now showing items 1-6 of 6

    • Alignment of uncalibrated images for multi-view classification 

      Arık, Sercan Ömer; Vuraf, E.; Frossard P. (IEEE, 2011)
      Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pair-wise similarity ...
    • Classification by voting feature intervals 

      Demiröz, Gülşen; Güvenir, H. Altay (Springer, 1997-04)
      A new classification algorithm called VFI (for Voting Feature Intervals) is proposed. A concept is represented by a set of feature intervals on each feature dimension separately. Each feature participates in the classification ...
    • A classification learning algorithm robust to irrelevant features 

      Güvenir, H. Altay (Springer, 1998-09)
      Presence of irrelevant features is a fact of life in many realworld applications of classification learning. Although nearest-neighbor classification algorithms have emerged as a promising approach to machine learning tasks ...
    • Estimating the chance of success in IVF treatment using a ranking algorithm 

      Güvenir, H. A.; Misirli, G.; Dilbaz, S.; Ozdegirmenci, O.; Demir, B.; Dilbaz, B. (Springer, 2015)
      In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of ...
    • Two-tier tissue decomposition for histopathological image representation and classification 

      Gultekin, T.; Koyuncu, C. F.; Sokmensuer, C.; Gunduz Demir, C. (Institute of Electrical and Electronics Engineers, 2015)
      In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly ...
    • Voting features based classifier with feature construction and its application to predicting financial distress 

      Güvenir, H. A.; Çakır, M. (Pergamon Press, 2010)
      Voting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to ...