title: U-processes and preference learning.
abstract: Preference learning has caused great attention in machining learning. In this letter we propose a learning framework for pairwise loss based on empirical risk minimization of U-processes via Rademacher complexity. We first establish a uniform version of Bernstein inequality of U-processes of degree 2 via the entropy methods. Then we estimate the bound of the excess risk by using the Bernstein inequality and peeling skills. Finally, we apply the excess risk bound to the pairwise preference and derive the convergence rates of pairwise preference learning algorithms with squared loss and indicator loss by using the empirical risk minimization with respect to U-processes.
authors.affiliation: School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China hongli@mail.hust.edu.cn.
authors.forename: Hong
authors.initials: H
authors.lastname: Li
authors.forename: Chuanbao
authors.initials: C
authors.lastname: Ren
authors.forename: Luoqing
authors.initials: L
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D019277:Entropy; D006801:Humans; D007858:Learning / Q000502:physiology*; D010363:Pattern Recognition, Automated