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.
2002
Neural Networks for Signal processing
IEEE International Workshop on Neural Networks for Signal Processing
0780376161
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2454317
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 3
  • OpenAlex ND
social impact