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dc.contributor.authorYue, Yisongen_US
dc.date.accessioned2013-07-23T18:20:33Z
dc.date.available2013-07-23T18:20:33Z
dc.date.issued2011-01-31en_US
dc.identifier.otherbibid: 8213968
dc.identifier.urihttps://hdl.handle.net/1813/33486
dc.description.abstractRecent 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.language.isoen_USen_US
dc.titleNew Learning Frameworks For Information Retrievalen_US
dc.typedissertation or thesisen_US
thesis.degree.disciplineComputer Science
thesis.degree.grantorCornell Universityen_US
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Computer Science
dc.contributor.chairJoachims, Thorstenen_US
dc.contributor.committeeMemberLi, Pingen_US
dc.contributor.committeeMemberHopcroft, John Een_US
dc.contributor.committeeMemberKleinberg, Robert Daviden_US


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