Practical selection of SVM parameters and noise estimation for SVM regression
type: Item
instance of: scholarly article
title: Practical selection of SVM parameters and noise estimation for SVM regression
zbMATH Open document ID: 1075.68632
DOI: 10.1016/S0893-6080(03)00169-2
author: Vladimir Cherkassky
author: Yunqian Ma
published in: Neural Networks
publication date: 2004-11-23T00:00:00Z
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 2118399
zbMATH Keywords: Complexity control
zbMATH Keywords: Loss function
zbMATH Keywords: Parameter selection
zbMATH Keywords: Prediction accuracy
zbMATH Keywords: Support vector machine regression
zbMATH Keywords: VC theory
Wikidata QID: Q47303092
describes a project that uses: ElemStatLearn
MaRDI profile type: Publication
cites work: Comparison of Model Selection for Regression
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cites work: Robust Estimation of a Location Parameter
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description: scientific article; zbMATH DE number 2118399