Analysis to Neyman-Pearson classification with convex loss function

DOI10.1007/s10496-008-0018-3zbMath1164.62358OpenAlexW1981135886MaRDI QIDQ3538596

Zhao Sun, Min Han, Di-Rong Chen

Publication date: 24 November 2008

Published in: Analysis in Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10496-008-0018-3

zbMATH Keywords

Neyman-Pearson lemmaRademacher average

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) Nonparametric inference (62G99)

Related Items (1)

Neyman-Pearson classification: parametrics and sample size requirement

Cites Work

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• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• A Neyman–Pearson Approach to Statistical Learning
• Performance Measures for Neyman–Pearson Classification
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities

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