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The convergence rate of a regularized ranking algorithm. (English) Zbl 1252.68225
Summary: 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.

MSC:
68T05 Learning and adaptive systems in artificial intelligence
68Q32 Computational learning theory
62D05 Sampling theory, sample surveys
62F05 Asymptotic properties of parametric tests
46E22 Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces)
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