Generalization Bounds for Certain Class of Ranking Algorithm
The quality of ranking determines the success or failure of information retrieval and the goal of ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. We focus on a ranking setting which uses truth function to label each pair of instances and the ranking preferences are given randomly from some distributions on the set of possible undirected edge sets of a graph. The contribution of this paper is the given generalization bounds for such ranking algorithm via strong and weak stability. Such stabilities have lower demand than uniform stability and fit for more real applications.
W. Gao and Y. G. Zhang, "Generalization Bounds for Certain Class of Ranking Algorithm", Advanced Materials Research, Vol. 267, pp. 456-461, 2011