Feature selection for unsupervised learning applied to content-based image retrieval

This thesis explores the feature selection for unsupervised learning problem. We investigate the problem through our algorithm called FSSEM (Feature Subset Selection wrapped around Expectation-Maximization clustering) and through two different performance criteria for evaluating candidate feature su...

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Bibliographic Details

Main Author:	Dy, Jennifer Guani (Author)
Resource Type:	Electronic Resource
Language:	English
Published:	Ann Arbor Michigan ProQuest Information and Learning Company [2002]
Subjects:	Computer algorithms. Machine learning.
Online Access:	Full text access requires UP Webmail login