Learning rates for regularized classifiers using multivariate polynomial kernels

DOI10.1016/j.jco.2008.05.008zbMath1169.68043OpenAlexW2021340030MaRDI QIDQ958248

Hongzhi Tong, Di-Rong Chen, Li Zhong Peng

Publication date: 3 December 2008

Published in: Journal of Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jco.2008.05.008

zbMATH Keywords

reproducing kernel Hilbert spacespolynomial kernelslearning ratesBernstein-Durrmeyer polynomialsregularized classifiers

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (13)

Learning rates of kernel-based robust classification ⋮ The learning rate of \(l_2\)-coefficient regularized classification with strong loss ⋮ Approximation by multivariate Bernstein-Durrmeyer operators and learning rates of least-squares regularized regression with multivariate polynomial kernels ⋮ Finite rank kernels for multi-task learning ⋮ Estimation of convergence rate for multi-regression learning algorithm ⋮ A Note on Support Vector Machines with Polynomial Kernels ⋮ Learning Rates for Classification with Gaussian Kernels ⋮ Optimal rate of the regularized regression learning algorithm ⋮ The convergence rate for a \(K\)-functional in learning theory ⋮ REGULARIZED LEAST SQUARE REGRESSION WITH SPHERICAL POLYNOMIAL KERNELS ⋮ Error analysis of multicategory support vector machine classifiers ⋮ The learning rates of regularized regression based on reproducing kernel Banach spaces ⋮ Estimates of learning rates of regularized regression via polyline functions

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