DocumentCode
2199479
Title
An efficient SMO-like algorithm for multiclass SVM
Author
Aiolli, Fahio ; Sperduti, Alessandro
Author_Institution
Dipt. di Informatica, Pisa, Italy
fYear
2002
fDate
2002
Firstpage
297
Lastpage
306
Abstract
Starting from a reformulation of Cramer and Singer (see Journal of Machine Learning Research, vol.2, p.265-92, Dec. 2001) multiclass kernel machine, we propose a sequential minimal optimization (SMO) like algorithm for incremental and fast optimization of the Lagrangian. The proposed formulation allowed us to define very effective new pattern selection strategies which lead to better empirical results.
Keywords
optimisation; signal classification; virtual machines; cooling schemes; digit recognition datasets; efficient SMO-like algorithm; equential minimal optimization; fast optimization; incremental optimization; multiclass SVM; multiclass classification; multiclass kernel machine; pattern selection; Kernel; Lagrangian functions; Prototypes; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on
Print_ISBN
0-7803-7616-1
Type
conf
DOI
10.1109/NNSP.2002.1030041
Filename
1030041
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