A Pattern Classification Method Based on a Space-Variant CNN Template

Author

Costantini, G. ; Casali, D. ; Carota, M.

Author_Institution

Departement of Electron. Eng., Rome Univ.

fYear

2006

fDate

28-30 Aug. 2006

Firstpage

1

Lastpage

5

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 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

Keywords

cellular neural nets; pattern classification; pattern clustering; bi-dimensional patterns; cellular neural network; pattern classification; pattern clustering; space-variant CNN template; unsupervised classification; Cellular neural networks; Classification algorithms; Clustering algorithms; Data engineering; Density functional theory; Electronic mail; Hardware; Neural networks; Partitioning algorithms; Pattern classification; Cellular Neural Networks; Clustering; Pattern Classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Cellular Neural Networks and Their Applications, 2006. CNNA '06. 10th International Workshop on

Conference_Location

Istanbul

Print_ISBN

1-4244-0639-0

Electronic_ISBN

1-4244-0640-4

Type

conf

DOI

10.1109/CNNA.2006.341633

Filename

4145873