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Tomaso Poggio. Zacharia, Giorgos, 1974- Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2009-10-01T15:38:38Z 2009-10-01T15:38:38Z 2009 2009 http://hdl.handle.net/1721.1/47753 428817925 Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. Includes bibliographical references (leaves 119-127). This thesis describes an investigation of regularized algorithms for ranking problems for user preferences and information retrieval problems. We utilize regularized manifold algorithms to appropriately incorporate data from related tasks. This investigation was inspired by personalization challenges in both user preference and information retrieval ranking problems. We formulate the ranking problem of related tasks as a special case of semi-supervised learning. We examine how to incorporate instances from related tasks, with the appropriate penalty in the loss function to optimize performance on the hold out sets. We present a regularized manifold approach that allows us to learn a distance metric for the different instances directly from the data. This approach allows incorporation of information from related task examples, without prior estimation of cross-task coefficient covariances. We also present applications of ranking problems in two text analysis problems: a) Supervise content-word learning, and b) Company Entity matching for record linkage problems. by Giorgos Zacharia. Ph.D. 127 leaves eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Regularized algorithms for ranking, and manifold learning for related tasks Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="1d9f639a-ff96-4e51-8e3f-5a2d960a94ea"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Regularized algorithms for ranking, and manifold learning for related tasks</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2009</PublicationDate> <Authors> <Author> <DisplayName>Zacharia, Giorgos, 1974-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>This thesis describes an investigation of regularized algorithms for ranking problems for user preferences and information retrieval problems. We utilize regularized manifold algorithms to appropriately incorporate data from related tasks. This investigation was inspired by personalization challenges in both user preference and information retrieval ranking problems. We formulate the ranking problem of related tasks as a special case of semi-supervised learning. We examine how to incorporate instances from related tasks, with the appropriate penalty in the loss function to optimize performance on the hold out sets. We present a regularized manifold approach that allows us to learn a distance metric for the different instances directly from the data. This approach allows incorporation of information from related task examples, without prior estimation of cross-task coefficient covariances. We also present applications of ranking problems in two text analysis problems: a) Supervise content-word learning, and b) Company Entity matching for record linkage problems.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>