New Learning Frameworks For Information Retrieval
| dc.contributor.author | Yue, Yisong | en_US |
| dc.contributor.chair | Joachims, Thorsten | en_US |
| dc.contributor.committeeMember | Li, Ping | en_US |
| dc.contributor.committeeMember | Hopcroft, John E | en_US |
| dc.contributor.committeeMember | Kleinberg, Robert David | en_US |
| dc.date.accessioned | 2013-07-23T18:20:33Z | |
| dc.date.available | 2013-07-23T18:20:33Z | |
| dc.date.issued | 2011-01-31 | en_US |
| dc.description.abstract | Recent advances in machine learning have enabled the training of increasingly complex information retrieval models. This dissertation proposes principled approaches to formalize the learning problems for information retrieval, with an eye towards developing a unified learning framework. This will conceptually simplify the overall development process, making it easier to reason about higher level goals and properties of the retrieval system. This dissertation advocates two complementary approaches, structured prediction and interactive learning, to learn feature-rich retrieval models that can perform well in practice. | en_US |
| dc.identifier.other | bibid: 8213968 | |
| dc.identifier.uri | https://hdl.handle.net/1813/33486 | |
| dc.language.iso | en_US | en_US |
| dc.title | New Learning Frameworks For Information Retrieval | en_US |
| dc.type | dissertation or thesis | en_US |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Cornell University | en_US |
| thesis.degree.level | Doctor of Philosophy | |
| thesis.degree.name | Ph. D., Computer Science |
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