This is the first book treating the fields of supervised,
semi-supervised and unsupervised machine learning collectively. The
book presents both the theory and the algorithms for mining huge
data sets using support vector machines (SVMs) in an iterative way.
It demonstrates how kernel based SVMs can be used for
dimensionality reduction and shows the similarities and differences
between the two most popular unsupervised techniques.
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