Learning to rank in supervised and unsupervised settings using convexity and monotonicity
This dissertation addresses the task of learning to rank, both in the supervised and unsupervised settings, by exploiting the interplay of convex functions, monotonic mappings and their fixed points. In the supervised setting of learning to rank, one wishes to learn from examples of correctly ordere...
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| Format: | Others |
| Language: | en_US |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/2152/21154 |