Scaling the kernel function based on the separating boundary in input space: a data-dependent way for improving the performance of kernel methods
type: Item
instance of: scholarly article
title: Scaling the kernel function based on the separating boundary in input space: a data-dependent way for improving the performance of kernel methods
zbMATH Open document ID: 1239.68065
author: Jiancheng Sun
author: Xiaohe Li
author: Jianguo Luo
author: Yaohui Bai
author: Yong Yang
published in: Information Sciences
publication date: 2012-06-08T00:00:00Z
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6043926
zbMATH Keywords: kernel methods
zbMATH Keywords: Riemannian geometry
zbMATH Keywords: classification
zbMATH Keywords: conformal transformation
describes a project that uses: UCI-ml
MaRDI profile type: Publication
full work available at URL: j.ins.2011.08.028
OpenAlex ID: W1975707885
cites work: Choosing multiple parameters for support vector machines
cites work: Introduction to algorithms
cites work: Neighborhood Property–Based Pattern Selection for Support Vector Machines
cites work: Q4935997
cites work: Induction of multiple fuzzy decision trees based on rough set technique
cites work: Conformal transformation of kernel functions: A data-dependent way to improve support vector machine classifiers
cites work: Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space
cites work: Structured large margin machines: sensitive to data distributions
cites work: Learning the kernel matrix by maximizing a KFD-based class separability criterion
DOI: 10.1016/J.INS.2011.08.028
description: scientific article; zbMATH DE number 6043926