A Pattern Classification Method Based on a Space-Variant CNN Template
Giovanni Costantini, Daniele Casali, Massimo Carota · 2006
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 nonlinearly 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