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      • Dept. of Computer Engineering - Master's degree
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      •   BUIR Home
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      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      Learning with feature partitions

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      Author
      Şirin, İzzet
      Advisor
      Güvenir, Halil Altay
      Date
      1993
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      This 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.
      Keywords
      Machine learning
      inductive learning
      incremental learning
      supervised learning
      feature partitioning
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      http://hdl.handle.net/11693/17508
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      • Dept. of Computer Engineering - Master's degree 516
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