Min-max classifiers: Learnability, design and application

Ping-Fai Yang, Petros Maragos
1995 Pattern Recognition  
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 of
more » ... d min-max functions are shown to be learnable, generalizing the learnability results for the corresponding classes of Boolean functions. We also propose a LMS algorithm for the practical training of these pattern classifiers. Experimental results using the LMS algorithm for handwritten character recognition are promising. For example, in our experiments the min-max classifiers were able to achieve error rates that are comparable or better than those generated using neural networks. The major advantage of min max classifiers compared to neural networks is their simplicity and the faster convergence of their training algorithm. Pattern classification Character recognition Machine learning Mathematical morphology Image processing
doi:10.1016/0031-3203(94)00161-e fatcat:ewzeweoxfbfi3ek5535kaq3un4