Error analysis of classification learning algorithms based on LUMs loss

DOI10.3934/mfc.2022028zbMath1527.68178OpenAlexW4292615554MaRDI QIDQ6112861

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Publication date: 7 August 2023

Published in: Mathematical Foundations of Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mfc.2022028

zbMATH Keywords

classificationlearning theoryerror boundlearning rateLUMs loss

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22)

Cites Work

• Unnamed Item
• Distance-Weighted Discrimination
• Approximation analysis of learning algorithms for support vector regression and quantile regression
• Estimating conditional quantiles with the help of the pinball loss
• Statistical behavior and consistency of classification methods based on convex risk minimization.
• Optimal aggregation of classifiers in statistical learning.
• Quantitative convergence analysis of kernel based large-margin unified machines
• Learning rates of least-square regularized regression
• Learning theory estimates via integral operators and their approximations
• Consistency of Support Vector Machines and Other Regularized Kernel Classifiers
• The influence of observations on misclassification probability estimates in linear discriminant analysis
• Another Look at Distance-Weighted Discrimination
• Comparison theorems on large-margin learning
• Learning theory of minimum error entropy under weak moment conditions
• Hard or Soft Classification? Large-Margin Unified Machines
• Convexity, Classification, and Risk Bounds
• Theory of Reproducing Kernels

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