Learnability of min-max pattern classifiers
Ping-Fai Yang, Petros A. Maragos · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
This paper introduces the class of thresholded min-max functions and studies their learning under the probably approximately correct (PAC) model introduced by Valiant. These functions can be used as pattern classifiers of both real-valued and binary-valued feature vectors. They are a lattice-theoretic generalization of Boolean functions and are also related to three-layer perceptrons and morphological signal operators. Several subclasses of the thresholded min- max functions are shown to be learnable under the PAC model.