Improved classification rates under refined margin conditions

DOI10.1214/18-EJS1406zbMath1473.62211arXiv1610.09109OpenAlexW2963705276MaRDI QIDQ1746541

Ingrid Blaschzyk, Ingo Steinwart

Publication date: 25 April 2018

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.09109

zbMATH Keywords

classificationstatistical learningfast rates of convergenceexcess riskhistogram rule

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Intrinsic Dimension Adaptive Partitioning for Kernel Methods ⋮ Unnamed Item ⋮ Adaptive learning rates for support vector machines working on data with low intrinsic dimension ⋮ Unnamed Item

Cites Work

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• Classification algorithms using adaptive partitioning
• Risk bounds for statistical learning
• Fully adaptive density-based clustering
• Fast learning rates for plug-in classifiers
• Support Vector Machines
• On the Rate of Convergence of Local Averaging Plug-In Classification Rules Under a Margin Condition
• Random forests

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