Supervised Clustering With Structural Svms
| dc.contributor.author | Finley, Thomas W. | en_US |
| dc.date.accessioned | 2009-05-22T18:27:39Z | |
| dc.date.available | 2009-05-22T18:27:39Z | |
| dc.date.issued | 2009-05-22T18:27:39Z | |
| dc.description.abstract | Supervised 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.identifier.other | bibid: 6630873 | |
| dc.identifier.uri | https://hdl.handle.net/1813/12819 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Structural Svms | en_US |
| dc.title | Supervised Clustering With Structural Svms | en_US |
| dc.type | dissertation or thesis | en_US |
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