A novel algorithm for unsupervised classification of datasets made up of integer valued patterns by means of Cellular Neural Network (CNN) is proposed. The adopted CNN is n-dimensional and is based on a space-variant template - neighborhood order 1 - to cluster n-dimensional datasets. The choice of a CNN architecture allows a straightforward hardware implementation, particularly suited for bi-dimensional patterns.
Costantini, G., Casali, D., Carota, M. (2005). supervised Pattern Classification by means of Cellular Neural Networks. ??????? it.cilea.surplus.oa.citation.tipologie.CitationProceedings.prensentedAt ??????? 9th WSEAS International Conference on CIRCUITS (ICC 2005).
supervised Pattern Classification by means of Cellular Neural Networks
COSTANTINI, GIOVANNI;
2005-01-01
Abstract
A novel algorithm for unsupervised classification of datasets made up of integer valued patterns by means of Cellular Neural Network (CNN) is proposed. The adopted CNN is n-dimensional and is based on a space-variant template - neighborhood order 1 - to cluster n-dimensional datasets. The choice of a CNN architecture allows a straightforward hardware implementation, particularly suited for bi-dimensional patterns.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
