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

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)
Format: 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