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Specification and Simulation of Statistical Query Algorithms for Efficiency and Noise Tolerance
 

Summary: Specification and Simulation of Statistical Query
Algorithms for Efficiency and Noise Tolerance
Javed A. Aslam \Lambda
Department of Computer Science
Dartmouth College
Hanover, NH 03755
Scott E. Decatur y
Laboratory for Computer Science
Massachusetts Institute of Technology
Cambridge, MA 02139
\Lambda Portions of this work were performed while the author was at Harvard University and supported by Air
Force Contract F49620­92­J­0466 and while the author was at MIT and supported by DARPA Contract
N00014­87­K­825 and by NSF Grant CCR­89­14428. Author's current net address: jaa@cs.dartmouth.edu
y This work was performed while the author was at Harvard University and supported by an NDSEG Doc­
toral Fellowship and by NSF Grant CCR­92­00884. Author's current net address: sed@dimacs.rutgers.edu
1

Abstract
A recent innovation in computational learning theory is the statistical query (SQ)
model. The advantage of specifying learning algorithms in this model is that SQ

  

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

 

Collections: Computer Technologies and Information Sciences