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Appearing in 34th Symposium on Foundations of Computer Science, 1993 General Bounds on Statistical Query Learning
 

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

  

Source: Aslam, Javed - College of Computer Science, Northeastern University

 

Collections: Computer Technologies and Information Sciences