Koç, Hakime Ünsal2016-01-082016-01-081995http://hdl.handle.net/11693/17727Ankara : Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent University, 1995.Thesis (Master's) -- -Bilkent University, 1995.Includes bibliographical references leaves 83-88.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 this incremental, inductive and supervised learning method, the basic unit of the representation is an interval. The COFI algorithm learns the projections of the intervals in each class dimension for each feature. An interval is initially a point on a class dimension, then it can be expanded through generalization. No specialization of intervals is done on class dimensions by this algorithm. Classification in the COFI algorithm is based on a majority voting among the local predictions that are made individually by each feature.xiii, 108 leavesEnglishinfo:eu-repo/semantics/openAccessmachine learningsupervised learninginductive learningincremental learningoverlapping feature intervalsconcept descriptionQ325.7 .K63 1995Computational learning theory.Machine learning.Expert systems (Computer science).Information retrieval.Knowledge representation (Information theory).Artificial intelligence.Thesis