An efficient Gaussian kernel optimization based on centered kernel polarization criterion

DOI10.1016/j.ins.2015.06.010zbMath1390.68557OpenAlexW577101549MaRDI QIDQ1750031

Wenjian Wang, Meng Tian

Publication date: 17 May 2018

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2015.06.010

zbMATH Keywords

pattern classificationGaussian kernelkernel selectioncentered kernel polarizationlocal multiclass centered kernel polarization

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (3)

Centered kernel alignment inspired fuzzy support vector machine ⋮ Efficient learning of supervised kernels with a graph-based loss function ⋮ A feature-weighted SVR method based on kernel space feature

Uses Software

• UCI-ml
• LIBSVM
• Delve Datasets

Cites Work

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• Alignment based kernel learning with a continuous set of base kernels
• Optimizing the Gaussian kernel function with the formulated kernel target alignment criterion for two-class pattern classification
• An efficient kernel matrix evaluation measure
• Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space
• Gaussian kernel optimization for pattern classification
• Multiple kernel clustering based on centered kernel alignment
• The Euler-Maclaurin and Taylor Formulas: Twin, Elementary Derivations
• Learning by Kernel Polarization
• Radius Margin Bounds for Support Vector Machines with the RBF Kernel
• Algorithmic Learning Theory
• Choosing multiple parameters for support vector machines

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