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General bounds on statistical query learning and PAC learning with noise via hypothesis boosting

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2 Author(s)
Aslam, J.A. ; Lab. for Comput. Sci., MIT, Cambridge, MA, USA ; Decatur, S.E.

We derive general bounds on the complexity of learning in the statistical query model and in the PAC model with classification noise. We do so by considering the problem of boosting the accuracy of weak learning algorithms which fall within the statistical query model. This new model was introduced by M. Kearns (1993) to provide a general framework for efficient PAC learning in the presence of classification noise

Published in:

Foundations of Computer Science, 1993. Proceedings., 34th Annual Symposium on

Date of Conference:

3-5 Nov 1993