Optimizing resources in model selection for support vector machine

DOI10.1016/j.patcog.2006.06.012zbMath1119.68143OpenAlexW2136130862MaRDI QIDQ856459

Mathias M. Adankon, Mohamed Cheriet

Publication date: 7 December 2006

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2006.06.012

zbMATH Keywords

model selectionkernelSVMhyperparametersoptimizing time

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

A neural network approach for the real-time detection of faults ⋮ Model selection for the LS-SVM. Application to handwriting recognition ⋮ CCH-based geometric algorithms for SVM and applications

Cites Work

• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Radius Margin Bounds for Support Vector Machines with the RBF Kernel
• Soft margins for AdaBoost
• Choosing multiple parameters for support vector machines
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