Ideal kernel tuning: Fast and scalable selection of the radial basis kernel spread for support vector classification

Author:

Akram-Ali-Hammouri Ziad,Fernández-Delgado Manuel,Albtoush Audi,Cernadas Eva,Barro Senén

Funder

European Regional Development Fund

Xunta de Galicia Consellería de Educacion e Ordenación Universitaria

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference19 articles.

1. Support-vector networks;Cortes;Mach. Learn.,1995

2. Do we need hundreds of classifiers to solve real world classification problems?;Fernández-Delgado;J. Mach. Learn. Res.,2014

3. C.-W. Hsu, C.-C. Chang, C.-J. Lin, et al., A practical guide to support vector classification (2003).

4. Width optimization of RBF kernels for binary classification of support vector machines: A density estimation-based approach;Menezes;Pattern Recogn. Lett.,2019

5. Evolutionary tuning of multiple SVM parameters;Friedrichs;Neurocomputing,2005

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1. Robust support function machines for set-valued data classification;International Journal of Approximate Reasoning;2024-11

2. Closed-Form Gaussian Spread Estimation for Small and Large Support Vector Classification;IEEE Transactions on Neural Networks and Learning Systems;2024

3. Power of kernel functions, its benefits, and limitations;2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC);2023-02-03

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