A novel algorithm for unsupervised classification of datasets made up of integer valued patterns by means of Cellular Neural Network (CNN) is proposed. The algorithm is suited both for linearly separable and non linearly, separable data sets. 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. (2006). A pattern classification method based on a spape-variant CNN template. In Proceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and Their Applications (pp.216-220). NEW YORK : IEEE [10.1109/CNNA.2006.341633].
A pattern classification method based on a spape-variant CNN template
COSTANTINI, GIOVANNI;
2006-01-01
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 algorithm is suited both for linearly separable and non linearly, separable data sets. 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.
