Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings

DOI10.1007/s10463-019-00727-1zbMath1465.62185OpenAlexW2959802665WikidataQ127477433 ScholiaQ127477433MaRDI QIDQ2023463

Makoto Aoshima, Yugo Nakayama, Kazuyoshi Yata

Publication date: 3 May 2021

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-019-00727-1

zbMATH Keywords

geometric representationHDLSSimbalanced dataradial basis function kernel

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Statistical aspects of big data and data science (62R07)

Related Items (5)

Support vector machine and optimal parameter selection for high-dimensional imbalanced data ⋮ Random forest kernel for high-dimension low sample size classification ⋮ Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings ⋮ Geometric classifiers for high-dimensional noisy data ⋮ Asymptotic properties of distance-weighted discrimination and its bias correction for high-dimension, low-sample-size data

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