Şirin, İzzet2016-01-082016-01-081993http://hdl.handle.net/11693/17508Ankara : Department of Computer Engineering and Information Science and Institute of Engineering and Science, Bilkent Univ., 1993.Thesis (Master's) -- Bilkent University, 1993.Includes bibliographical references leaves 78-81This thesis presents a new methodology of learning from examples, based on feature partitioning. Classification by Feature Partitioning (CFP) is a particular implementation of this technique, which is an inductive, incremental, and supervised learning method. Learning in CFP is accomplished by storing the objects separately in each feature dimension as disjoint partitions of values. A partition, a basic unit of representation which is initially a point in the feature dimension, is expanded through generalization. The CFP algorithm specializes a partition by subdividing it into two subpartitions. Theoretical (with respect to PAC-model) and empirical evaluation of the CFP is presented and compared with some other similar techniques.xii, 88 leavesEnglishinfo:eu-repo/semantics/openAccessMachine learninginductive learningincremental learningsupervised learningfeature partitioningQ325.5 .S57 1993Machine learning.Inductive learning.Supervised learning (machine learning).Supervised learning.Thesis