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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.

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)
Full Text: DOI
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