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...

Ausführliche Beschreibung

Bibliographische Detailangaben
1. Verfasser: Dy, Jennifer Guani (VerfasserIn)
Format: Electronic Resource
Sprache:English
Veröffentlicht: Ann Arbor Michigan ProQuest Information and Learning Company [2002]
Schlagworte:
Computer algorithms.
Machine learning.
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