A general approach to classification problems

DOI10.1016/0020-0255(83)90047-6zbMath0584.62095OpenAlexW1995837385MaRDI QIDQ1069623

Gilbert G. Walter

Publication date: 1983

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0020-0255(83)90047-6

zbMATH Keywords

classificationrate of convergencedelta sequencesnonparametric density estimators

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05)

Cites Work

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• A classification procedure using the multiple Fourier series
• Asymptotically efficient solutions to the classification problem
• Estimation of a multivariate density function using delta sequences
• A finite sample distribution-free performance bound for local discrimination rules
• Probability density estimation using delta sequences
• On large deviations of the empiric D.F. of vector chance variables and a law of the iterated logarithm
• Multivariate binary discrimination by the kernel method
• Practical Approximate Solutions to Linear Operator Equations When the Data are Noisy
• Nonparametric discrimination procedures for non-normal distributions
• Sample-Based Classification Procedures Derived from Density Estimators

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