Title :
An efficient SMO-like algorithm for multiclass SVM
Author :
Aiolli, Fahio ; Sperduti, Alessandro
Author_Institution :
Dipt. di Informatica, Pisa, Italy
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;
Conference_Titel :
Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on
Print_ISBN :
0-7803-7616-1
DOI :
10.1109/NNSP.2002.1030041
