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Kernel Methods for Pattern Analysis by Nello Cristianini, John Shawe-Taylor

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8

Ranking, clustering and data visualisation

In this chapter we conclude our presentation of kernel-based pattern analysis algorithms by discussing three further common tasks in data analysis: ranking, clustering and data visualisation.

Ranking is the problem of learning a ranking function from a training set of ranked data. The number of ranks need not be specified though typically the training data comes with a relative ordering specified by assignment to one of an ordered sequence of labels.

Clustering is perhaps the most important and widely used method of unsupervised learning: it is the problem of identifying groupings of similar points ...

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