We consider polyhedral separation of sets as a possible tool in supervised classification. In particular, we focus on the optimization model introduced by Astorino and Gaudioso (J Optim Theory Appl 112(2):265-293, 2002) and adopt its reformulation in difference of convex (DC) form. We tackle the problem by adapting the algorithm for DC programming known as DCA. We present the results of the implementation of DCA on a number of benchmark classification datasets.
Polyhedral separation via difference of convex (DC) programming
Annabella Astorino;
2021
Abstract
We consider polyhedral separation of sets as a possible tool in supervised classification. In particular, we focus on the optimization model introduced by Astorino and Gaudioso (J Optim Theory Appl 112(2):265-293, 2002) and adopt its reformulation in difference of convex (DC) form. We tackle the problem by adapting the algorithm for DC programming known as DCA. We present the results of the implementation of DCA on a number of benchmark classification datasets.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
prod_451956-doc_166445.pdf
solo utenti autorizzati
Descrizione: Polyhedral separation via difference of convex (DC) programming
Tipologia:
Versione Editoriale (PDF)
Licenza:
Nessuna licenza dichiarata (non attribuibile a prodotti successivi al 2023)
Dimensione
228.41 kB
Formato
Adobe PDF
|
228.41 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
