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- W2258544123 abstract "In 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." @default.
- W2258544123 created "2016-06-24" @default.
- W2258544123 creator A5066581662 @default.
- W2258544123 date "2008-08-07" @default.
- W2258544123 modified "2023-09-26" @default.
- W2258544123 title "Transductive Ranking on Graphs" @default.
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