Learning rate of support vector machine for ranking

DOI10.1016/J.JAT.2014.08.004zbMath1358.68231OpenAlexW1978737776MaRDI QIDQ468458

Di-Rong Chen, Heng Chen

Publication date: 7 November 2014

Published in: Journal of Approximation Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jat.2014.08.004

zbMATH Keywords

\(U\)-statisticsupport vector machineranking ruleexcess risk

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Optimality of regularized least squares ranking with imperfect kernels ⋮ Statistical performance of optimal scoring in reproducing kernel Hilbert spaces ⋮ Debiased magnitude-preserving ranking: learning rate and bias characterization

Cites Work

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• The convergence rate of a regularized ranking algorithm
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• Best choices for regularization parameters in learning theory: on the bias-variance problem.
• Ranking and empirical minimization of \(U\)-statistics
• Learning rates of least-square regularized regression
• Learning theory estimates via integral operators and their approximations
• Learning Theory
• Support Vector Machines
• 10.1162/1532443041424319
• Convexity, Classification, and Risk Bounds
• Theory of Reproducing Kernels

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