Gaussian kernel optimization for pattern classification

DOI10.1016/j.patcog.2008.11.024zbMath1183.68552OpenAlexW2118439480WikidataQ62040782 ScholiaQ62040782MaRDI QIDQ1015178

Haiping Lu, Konstantinos N. Plataniotis, Juwei Lu, Jie Wang

Publication date: 7 May 2009

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2008.11.024

zbMATH Keywords

pattern classificationGaussian kernelsmall sample sizekernel optimization

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (7)

A new robust model of one-class classification by interval-valued training data using the triangular kernel ⋮ Tuning of the hyperparameters for \(L2\)-loss SVMs with the RBF kernel by the maximum-margin principle and the jackknife technique ⋮ Learning with infinitely many features ⋮ Fast Gaussian kernel learning for classification tasks based on specially structured global optimization ⋮ An efficient Gaussian kernel optimization based on centered kernel polarization criterion ⋮ Kernel-based hard clustering methods in the feature space with automatic variable weighting ⋮ Optimizing the Gaussian kernel function with the formulated kernel target alignment criterion for two-class pattern classification

Uses Software

• UCI-ml
• FERET

Cites Work

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• On solving the face recognition problem with one training sample per subject
• Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation
• Gradient-Based Adaptation of General Gaussian Kernels
• Choosing multiple parameters for support vector machines

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