Adaptive resolution Min-Max classifier

In the present paper a new neuro-fuzzy classifier, inspired by the Simpson's Min-Max model, is presented 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, increasing the generalization capability in accordance with Learning Theory paradigms. Several rests are presented for illustration.