Learning with feature partitions
Date
1993
Authors
Editor(s)
Advisor
Güvenir, Halil Altay
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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.
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Degree Discipline
Computer Engineering
Degree Level
Master's
Degree Name
MS (Master of Science)
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Language
English