Fauvel, Mathieu Introduction to Kernel Methods: Classification of Multivariate Data. (2016) EAS Publications Series, 77. 171-193. ISSN 1633-4760
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(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 357kB | |
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(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 662kB |
Official URL: http://dx.doi.org/10.1051/eas/1677008
Abstract
In this chapter, kernel methods are presented for the classification of multivariate data. An introduction example is given to enlighten the main idea of kernel methods. Then emphasis is done on the Support Vector Machine. Structural risk minimization is presented, and linear and non-linear SVM are described. Finally, a full example of SVM classification is given on simulated hyperspectral data.
| Item Type: | Article |
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| Additional Information: | Thanks to EDP Sciences editor. The definitive version is available at https://www.eas-journal.org The original PDF of the article can be found at EAS Journal website : https://www.eas-journal.org/articles/eas/pdf/2016/01/eas1677008.pdf |
| ProdINRA Id: | 355938 |
| Audience (journal): | National peer-reviewed journal |
| Institution: | French research institutions > Institut National de la Recherche Agronomique - INRA (FRANCE) Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE) |
| Laboratory name: | |
| Statistics: | download |
| Deposited On: | 15 Jun 2017 08:16 |
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