J. Jackson & M. Craven (1996).
Learning Sparse Perceptrons. Advances in Neural Information Processing Systems, pp. 654-662, Denver, CO. MIT Press.

This publication is available in PDF and available in postscript.

Abstract:

We introduce a new algorithm designed to learn sparse perceptrons over input representations which include high-order features. Our algorithm, which is based on a hypothesis-boosting method, is able to PAC-learn a relatively natural class of target concepts. Moreover, the algorithm appears to work well in practice: on a set of three problem domains, the algorithm produces classifiers that utilize small numbers of features yet exhibit good generalization performance. Perhaps most importantly, our algorithm generates concept descriptions that are easy for humans to understand.

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