Show simple item record

dc.contributor.advisorRonitt Rubinfeld
dc.contributor.authorAgarwal, Shivanien_US
dc.contributor.otherTheory of Computationen_US
dc.date.accessioned2008-08-14T17:15:02Z
dc.date.available2008-08-14T17:15:02Z
dc.date.issued2008-08-07
dc.identifier.urihttp://hdl.handle.net/1721.1/41938
dc.description.abstractIn ranking, one is given examples of order relationships among objects, and the goal is to learn from these examples a real-valued ranking function that induces a ranking or ordering over the object space. We consider the problem of learning such a ranking function in a transductive, graph-based setting, where the object space is finite and is represented as a graph in which vertices correspond to objects and edges encode similarities between objects. Building on recent developments in regularization theory for graphs and corresponding Laplacian-based learning methods, we develop an algorithmic framework for learning ranking functions on graphs. We derive generalization bounds for our algorithms in transductive models similar to those used to study other transductive learning problems, and give experimental evidence of the potential benefits of our framework.en_US
dc.format.extent27 p.en_US
dc.relation.ispartofseriesMIT-CSAIL-TR-2008-051en_US
dc.subjectGraph-based learningen_US
dc.subjectTransductive learningen_US
dc.subjectRankingen_US
dc.subjectGraph regularizationen_US
dc.titleTransductive Ranking on Graphsen_US


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record