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.

Adaptive Resolution Min-Max Classifier / Rizzi, Antonello; FRATTALE MASCIOLI, Fabio Massimo; Martinelli, Giuseppe. - STAMPA. - 2:(1998), pp. 1435-1440. ((Intervento presentato al convegno International Conference on Fuzzy Systems (WCCI/FUZZ-IEEE ’98) tenutosi a Anchorage, Alaska, USA nel 4-9 May 1998 [10.1109/FUZZY.1998.686330].

Adaptive Resolution Min-Max Classifier

RIZZI, Antonello;FRATTALE MASCIOLI, Fabio Massimo;MARTINELLI, Giuseppe
1998

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.
0-7803-4863-X
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/246042
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