DocumentCode
2307381
Title
A new multi-class SVM based on a uniform convergence result
Author
Guermeur, Yann ; Elisseeff, André ; Paugam-Moisy, Hélène
Author_Institution
LORIA, Vandoeuvre-les-Nancy, France
Volume
4
fYear
2000
fDate
2000
Firstpage
183
Abstract
We introduce a support vector machine devoted to the approximation of multi-class discriminant functions. Its training procedure consists in minimizing an expression of the guaranteed risk. This bound is significantly tighter than the former ones, which should make the implementation of the structural risk minimization inductive principle in the context of multi-class discrimination better grounded
Keywords
convergence; function approximation; learning (artificial intelligence); matrix algebra; neural nets; pattern recognition; probability; guaranteed risk; multi-class discriminant functions; multi-class discrimination; structural risk minimization inductive principle; support vector machine; training procedure; uniform convergence result; Convergence; Pattern recognition; Quadratic programming; Risk management; Support vector machines; Upper bound; Virtual colonoscopy;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.860770
Filename
860770
Link To Document