A stable hyperparameter selection for the Gaussian RBF kernel for discrimination

DOI10.1002/SAM.10073OpenAlexW3083102089MaRDI QIDQ4969712

Jeongyoun Ahn

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.10073

zbMATH Keywords

classificationstabilitykernel methodsupport vector machinesdata embeddinghyperparameter selection

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Cites Work

• Title not available (Why is that?)
• Kernel methods in machine learning
• Bandwidth choice for nonparametric classification
• Theoretical foundations of the potential function method in pattern recognition learning
• An introduction to support vector machines and other kernel-based learning methods.
• Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
• Choosing multiple parameters for support vector machines

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