ℓ1-Norm support vector machine for ranking with exponentially strongly mixing sequence

DOI10.1142/S0219691314610013zbMath1366.62037MaRDI QIDQ2930104

Shouyou Huang, Di-Rong Chen

Publication date: 18 November 2014

Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)

zbMATH Keywords

support vector machineU-statisticranking ruleexcess riskexponentially strongly mixing sequence

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistical ranking and selection procedures (62F07)

Cites Work

• Limit theorems for \(U\)-processes
• The convergence rate of a regularized ranking algorithm
• Fast rates for support vector machines using Gaussian kernels
• Analysis of support vector machines regression
• Rates of convergence for empirical processes of stationary mixing sequences
• Support vector machines regression with \(l^1\)-regularizer
• Learning and generalisation. With applications to neural networks.
• Ranking and empirical minimization of \(U\)-statistics
• Classifiers of support vector machine type with \(\ell_1\) complexity regularization
• Learning theory estimates via integral operators and their approximations
• Learning Theory
• Minimum complexity regression estimation with weakly dependent observations
• An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
• Shannon sampling and function reconstruction from point values
• Convexity, Classification, and Risk Bounds

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