Title :
Adaptive resolution min-max classifier
Author :
Rizzi, A. ; Mascioli, F. M Frattale ; Martinelli, G.
Author_Institution :
INFOCOM Dept., Rome Univ., Italy
Abstract :
This paper presents a new neuro-fuzzy classifier, inspired by the Simpson´s (1992, 1993) min-max model. By relying on a constructive approach, it overcomes some undesired properties of the original min-max algorithm. In particular, training result does 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. Consequently, the new algorithm yields less complex networks, thus increasing the generalization capability in accordance with learning theory paradigms. Several tests are presented for illustration
Keywords :
fuzzy neural nets; generalisation (artificial intelligence); learning (artificial intelligence); minimax techniques; pattern classification; constructive algorithms; fuzzy neural networks; generalization; learning theory; min-max neural network; neural fuzzy classifier; pattern classification; Artificial intelligence; Classification algorithms; Clustering algorithms; Complex networks; Neural networks; Partitioning algorithms; Pattern recognition; Process control; Size control; Testing;
Conference_Titel :
Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4863-X
DOI :
10.1109/FUZZY.1998.686330
