Now showing items 1-9 of 9

    • Classification by feature partitioning 

      Guvenir, H. A.; Şirin, İ. (Springer/Kluwer Academic Publishers-Plenum Publishers, 1996)
      This paper presents a new form of exemplar-based learning, based on a representation scheme called jfaliirf parluinning, and a panitular implementation of this technique called CFF (for Classification by feature Partioning). ...
    • Clustered linear regression 

      Ari, B.; Güvenir, H. A. (Elsevier, 2002)
      Clustered linear regression (CLR) is a new machine learning algorithm that improves the accuracy of classical linear regression by partitioning training space into subspaces. CLR makes some assumptions about the domain and ...
    • Concept representation with overlapping feature intervals 

      Güvenir, H. A.; Koç, H. G. (Taylor & Francis Inc., 1998)
      This article presents a new form of exemplar-based learning method, based on overlapping feature intervals. In this model, a concept is represented by a collection of overlappling intervals for each feature and class. ...
    • An eager regression method based on best feature projections 

      Aydın, Tolga; Güvenir, H. Altay (Springer, Berlin, Heidelberg, 2001)
      This paper describes a machine learning method, called Regression by Selecting Best Feature Projections (RSBFP). In the training phase, RSBFP projects the training data on each feature dimension and aims to find the ...
    • Instance-based regression by partitioning feature projections 

      Uysal, İ.; Güvenir, H. A. (Springer, 2004)
      A new instance-based learning method is presented for regression problems with high-dimensional data. As an instance-based approach, the conventional method, KNN, is very popular for classification. Although KNN performs ...
    • Learning problem solving strategies using refinement and macro generation 

      Güvenir, H. A.; Ernst, G. W. (Elsevier BV, 1990)
      In this paper we propose a technique for learning efficient strategies for solving a certain class of problems. The method, RWM, makes use of two separate methods, namely, refinement and macro generation. The former is a ...
    • Maximizing benefit of classifications using feature intervals 

      İkizler, Nazlı; Güvenir, H. Altay (Springer, Berlin, Heidelberg, 2003)
      There is a great need for classification methods that can properly handle asymmetric cost and benefit constraints of classifications. In this study, we aim to emphasize the importance of classification benefits by means ...
    • An overview of regression techniques for knowledge discovery 

      Uysal, İ.; Güvenir, H. A. (Cambridge University Press, 1999)
      Predicting or learning numeric features is called regression in the statistical literature, and it is the subject of research in both machine learning and statistics. This paper reviews the important techniques and algorithms ...
    • Regression on feature projections 

      Guvenir, H. A.; Uysal, I. (Elsevier, 2000)
      This paper describes a machine learning method, called Regression on Feature Projections (RFP), for predicting a real-valued target feature, given the values of multiple predictive features. In RFP training is based on ...