This paper presents a novel neural network model, called similarity neural network (SNN), designed to learn similarity measures for pairs of patterns. The model guarantees to compute a non negative and symmetric measure, and shows good generalization capabilities even if a very small set of supervised examples is used for training. Preliminary experiments, carried out on some UCI datasets, are presented, showing promising results.
Melacci, S., Sarti, L., Maggini, M., Bianchini, M. (2008). A neural network approach to similarity learning. In Artificial Neural Networks in Pattern Recognition (pp.133-136). Berlin : Springer Verlag [10.1007/978-3-540-69939-2_13].
A neural network approach to similarity learning
MELACCI, STEFANO;SARTI, LORENZO;MAGGINI, MARCO;BIANCHINI, MONICA
2008-01-01
Abstract
This paper presents a novel neural network model, called similarity neural network (SNN), designed to learn similarity measures for pairs of patterns. The model guarantees to compute a non negative and symmetric measure, and shows good generalization capabilities even if a very small set of supervised examples is used for training. Preliminary experiments, carried out on some UCI datasets, are presented, showing promising results.
| File | Dimensione | Formato | |
|---|---|---|---|
|
ANNPR08.pdf
non disponibili
Tipologia:
Post-print
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
150.37 kB
Formato
Adobe PDF
|
150.37 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.
https://hdl.handle.net/11365/23166
Attenzione
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo