Learning with feature partitions
Author
Şirin, İzzet
Advisor
Güvenir, Halil Altay
Date
1993Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
111
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41
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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 learninginductive learning
incremental learning
supervised learning
feature partitioning
Permalink
http://hdl.handle.net/11693/17508Collections
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