| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4607375 | Journal of Approximation Theory | 2012 | 7 Pages |
Abstract
In this paper, we investigate the generalization performance of a regularized ranking algorithm in a reproducing kernel Hilbert space associated with least square ranking loss. An explicit expression for the solution via a sampling operator is derived and plays an important role in our analysis. Convergence analysis for learning a ranking function is provided, based on a novel capacity independent approach, which is stronger than for previous studies of the ranking problem.
Related Topics
Physical Sciences and Engineering
Mathematics
Analysis
Authors
Hong Chen,