DocumentCode
396668
Title
Design of support vector machine by adaptive aggregation
Author
Chacon, Oscar ; Litvintchev, Igor ; Alvarez, Ada ; Vazquez, Ernesto
Author_Institution
Graduate Program of Syst. Eng., Univ. Autonoma de Nuevo Leon, Mexico
Volume
3
fYear
2003
fDate
20-24 July 2003
Firstpage
2083
Abstract
This article provides a new algorithm to solve the design of classification machine, for linearly separable sets, based in support vectors. For large scale binary classification, an adaptive aggregation (AAM) procedure is executed so that the size of possible support vectors decrease, in each iteration, until convergence to maximum separation margin is achieved.
Keywords
convergence of numerical methods; iterative methods; learning (artificial intelligence); optimisation; pattern classification; support vector machines; adaptive aggregation; classification machine design; convergence; iterative methods; large scale binary classification; maximum separation margin; support vector machine design; support vectors; Active appearance model; Algorithm design and analysis; Convergence; Large-scale systems; Pattern recognition; Statistical learning; Statistics; Support vector machine classification; Support vector machines; Systems engineering and theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223729
Filename
1223729
Link To Document