Starting from a reformulation of Cramer & Singer Mul- ticlass Kernel Machine, we propose a Sequential Minimal Opti- mization (SMO) like algorithm for incremental and fast optimiza- tion of the lagrangian. The proposed formulation allowed us to dene very eective new pattern selection strategies which lead to better empirical results.

An Efficient SMO-like Algorithm for Multiclass SVM

AIOLLI, FABIO;SPERDUTI, ALESSANDRO
2002

Abstract

Starting from a reformulation of Cramer & Singer Mul- ticlass Kernel Machine, we propose a Sequential Minimal Opti- mization (SMO) like algorithm for incremental and fast optimiza- tion of the lagrangian. The proposed formulation allowed us to dene very eective new pattern selection strategies which lead to better empirical results.
Neural Networks for Signal processing
0780376161
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2454317
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