A novel formulation of orthogonal polynomial kernel functions for SVM classifiers: The Gegenbauer family-Reference-Cited by-同舟云学术

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A novel formulation of orthogonal polynomial kernel functions for SVM classifiers: The Gegenbauer family

Published:2018-12 Issue: Volume:84 Page:211-225
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Padierna Luis Carlos,Carpio Martín,Rojas-Domínguez Alfonso^ORCID,Puga Héctor,Fraire Héctor

Funder

National Council of Science and Technology of Mexico (CONACYT)

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference40 articles.

1. Kernel Methods for Pattern Analysis;Shawe-Taylor,2004

2. Kernel Methods for Pattern Analysis;Cristianini,2004

3. Support Vector Machines for Pattern Classification;Shigeo,2010

4. Support Vector Machines;Deng,2013

5. A review of optimization methodologies in support vector machines;Shawe-Taylor;Neurocomputing,2011

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