Supervised multidimensional scaling for visualization, classification, and bipartite ranking
Computational Statistics & Data Analysis
The Journal of Machine Learning Research
Maximum margin ranking algorithms for information retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Full length article: The convergence rate of a regularized ranking algorithm
Journal of Approximation Theory
Information Sciences: an International Journal
Learning theory approach to minimum error entropy criterion
The Journal of Machine Learning Research
Uniform convergence, stability and learnability for ranking problems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained much attention in machine learning. We study generalization properties of ranking algorithms using the notion of algorithmic stability; in particular, we derive generalization bounds for ranking algorithms that have good stability properties. We show that kernel-based ranking algorithms that perform regularization in a reproducing kernel Hilbert space have such stability properties, and therefore our bounds can be applied to these algorithms; this is in contrast with generalization bounds based on uniform convergence, which in many cases cannot be applied to these algorithms. Our results generalize earlier results that were derived in the special setting of bipartite ranking (Agarwal and Niyogi, 2005) to a more general setting of the ranking problem that arises frequently in applications.