In the present paper, a new algorithm to train Min-Max neural models is proposed. It is based on the ARC technique, which overcomes some undesired properties of the original Simpson’s algorithm. In particular, training results do not depend on pattern presentation order and hyperbox expansion is not limited by a fixed maximum size, so that it is possible to have different covering resolutions. ARC generates the optimal Min-Max network by a succession of hyperbox cuts. The generalization capability of ARC technique depends mostly on the adopted cutting strategy. A new recursive cutting procedure allows ARC technique to yield a better performance. Some real data benchmarks are considered for illustration.
A Recursive Algorithm for Fuzzy Min-Max Networks / Rizzi, Antonello; Panella, Massimo; FRATTALE MASCIOLI, Fabio Massimo; Martinelli, Giuseppe. - 6:(2000), pp. 541-546. (Intervento presentato al convegno IEEE-INNS-ENNS International Joint Conference on Neural Networks tenutosi a Como, Italia nel 24-27 luglio 2000) [10.1109/IJCNN.2000.859451].
A Recursive Algorithm for Fuzzy Min-Max Networks
RIZZI, Antonello;PANELLA, Massimo;FRATTALE MASCIOLI, Fabio Massimo;MARTINELLI, Giuseppe
2000
Abstract
In the present paper, a new algorithm to train Min-Max neural models is proposed. It is based on the ARC technique, which overcomes some undesired properties of the original Simpson’s algorithm. In particular, training results do not depend on pattern presentation order and hyperbox expansion is not limited by a fixed maximum size, so that it is possible to have different covering resolutions. ARC generates the optimal Min-Max network by a succession of hyperbox cuts. The generalization capability of ARC technique depends mostly on the adopted cutting strategy. A new recursive cutting procedure allows ARC technique to yield a better performance. Some real data benchmarks are considered for illustration.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
