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  Pattern Classification  All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000   with the permission of the authors and the publisher  Chapter 10 Unsupervised Learning & Clustering ã Introduction ã Mixture Densities and Identifiability ã ML Estimates ã  Application to Normal Mixtures ã K-means algorithm ã Unsupervised Bayesian Learning ã Data description and clustering ã Criterion function for clustering ã Hierarchical clustering ã The number of cluster problem and cluster validation ã On-line clustering ã Graph-theoretic methods ã PCA and ICA ã Low-dim reps and multidimensional scaling (self-organizing maps) ã Clustering and dimensionality reduction   Pattern Classification, Chapter 10 2 Introduction ã Previously, all our training samples were labeled: these samples were said “supervised”   ã Why are we interested in “unsupervised” procedures which use unlabeled samples? 1)Collecting and Labeling a large set of sample patterns can be costly 2)We can train with large amounts of (less expensive) unlabeled data  Then use supervision to label the groupings found, this is appropriate for large “data mining” applications where the contents of a large database are not known beforehand   Pattern Classification, Chapter 10 3 3)Patterns may change slowly with time  Improved performance can be achieved if classifiers running in a unsupervised mode are used 4)We can use unsupervised methods to identify features that will then be useful for categorization    „smart‟ feature extraction  5)We gain some insight into the nature (or structure) of the data   which set of classification labels?

Thesis Report

Jul 23, 2017
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