In this paper a new neural approach to clustering tasks in handwritten numeral recognition problems is compared to classical unsupervised neural networks techniques. The kernel of the proposed network is a neural unit able to perform clustering acting alone. The network is able to find directly dense zones of the input space without requiring competition and thus overcoming the major drain backs of classical unsupervised architectures.
Performance Evaluation of a Parallel Collision Control Unsupervised Neural Network / Acciani, G.; Chiarantoni, E.; Minenna, M.; Vacca, F.. - (1994), pp. 610-613. (Intervento presentato al convegno 37th Midwest Symposium on Circuits and Systems tenutosi a Lafayette, LA nel August 3-5, 1994) [10.1109/MWSCAS.1994.519369].
Performance Evaluation of a Parallel Collision Control Unsupervised Neural Network
G. Acciani;E. Chiarantoni;F. Vacca
1994-01-01
Abstract
In this paper a new neural approach to clustering tasks in handwritten numeral recognition problems is compared to classical unsupervised neural networks techniques. The kernel of the proposed network is a neural unit able to perform clustering acting alone. The network is able to find directly dense zones of the input space without requiring competition and thus overcoming the major drain backs of classical unsupervised architectures.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
