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dc.contributor.authorFinley, Thomas W.en_US
dc.date.accessioned2009-05-22T18:27:39Z
dc.date.available2009-05-22T18:27:39Z
dc.date.issued2009-05-22T18:27:39Z
dc.identifier.urihttp://hdl.handle.net/1813/12819
dc.description.abstractSupervised clustering is the problem of training clustering methods to produce desirable clusterings. Given sets of items and complete clusterings over these sets, a supervised clustering algorithm learns how to cluster future sets of items in a similar fashion, typically by changing the underlying similarity measure between item pairs. This work presents a general approach for training clustering methods such as correlation clustering and k-means/spectral clustering able to optimize to task-specific performance criteria using structural SVMs. We empirically and theoretically analyze our supervised clustering approach on a variety of datasets and clustering methods. This analysis also leads to general insights about structural SVMs beyond supervised clustering. Specifically, since clustering is a NP-hard task and the corresponding training problem likewise must make use of approximate inference during training of the parameters, we present a detailed theoretical and empirical analysis of the general use of approximations in structural SVM training.en_US
dc.language.isoen_USen_US
dc.subjectStructural Svmsen_US
dc.titleSupervised Clustering With Structural Svmsen_US
dc.typedissertation or thesisen_US


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