Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space

DOI10.1016/j.patcog.2008.08.030zbMath1162.68639OpenAlexW2153272791MaRDI QIDQ1010078

Kuo-Ping Wu, Sheng-De Wang

Publication date: 3 April 2009

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: http://ntur.lib.ntu.edu.tw//handle/246246/142303

zbMATH Keywords

support vector machinesSVMkernel parametersinter-cluster distances

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (8)

A nested heuristic for parameter tuning in support vector machines ⋮ An adaptive support vector regressor controller for nonlinear systems ⋮ Support vector machine with Dirichlet feature mapping ⋮ Scaling the kernel function based on the separating boundary in input space: a data-dependent way for improving the performance of kernel methods ⋮ Multi-class support vector machine optimized by inter-cluster distance and self-adaptive differential evolution ⋮ An efficient Gaussian kernel optimization based on centered kernel polarization criterion ⋮ Tuning kernel parameters for SVM based on expected square distance ratio ⋮ Nearest neighbors methods for support vector machines

Uses Software

• LIBSVM
• Torch

Cites Work

• Polynomial-time decomposition algorithms for support vector machines
• A method to make multiple hypotheses with high cumulative recognition rate using SVMs.
• Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
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