288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

December 2014, Vol. 26, No. 12, Pages 2896-2924
(doi: 10.1162/NECO_a_00674)
© 2014 Massachusetts Institute of Technology
U-Processes and Preference Learning
Article PDF (224.71 KB)

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.