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This paper introduces the class of min max classifiers. These are binary-valued functions that can be used as pattern classifiers of both real-valued and binary-valued feature vectors. They are also lattice-theoretic generalization of Boolean functions and are also related to feed-forward neural networks and morphological signal operators. We studied supervised learning of these classifiers under the Probably Approximately Correct (PAC) model proposed by Valiant. Several subclasses ofdoi:10.1016/0031-3203(94)00161-e fatcat:ewzeweoxfbfi3ek5535kaq3un4