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Summary: Appearing in 34th Symposium on Foundations of Computer Science, 1993
General Bounds on Statistical Query Learning
and PAC Learning with Noise via Hypothesis Boosting
Javed A. Aslam #
Laboratory for Computer Science
Massachusetts Institute of Technology
Cambridge, MA 02139
Scott E. Decatur +
Aiken Computation Laboratory
Harvard University
Cambridge, MA 02138
Abstract
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 Statis
tical Query model. This new model was introduced
by Kearns [12] to provide a general framework for ef
ficient PAC learning in the presence of classification
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