Learnability of min-max pattern classifiers

Ping-Fai Yang; Petros Maragos

doi:10.1117/12.50358

1 November 1991 Learnability of min-max pattern classifiers

Ping-Fai Yang, Petros Maragos

Author Affiliations +

Proceedings Volume 1606, Visual Communications and Image Processing '91: Image Processing; (1991) https://doi.org/10.1117/12.50358
Event: Visual Communications, '91, 1991, Boston, MA, United States

Abstract

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.

Citation Download Citation

Ping-Fai Yang and Petros Maragos "Learnability of min-max pattern classifiers", Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991); https://doi.org/10.1117/12.50358

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