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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 algorithms can be simulated in the PAC model, both in the absence and in the presence of noise. However, simulations of SQ algorithms in the PAC model have non-optimal time and sample complexities. In this paper, we introduce a new method for specifying statistical query algorithms based on a type of relative error and provide... (Update)
Context of citations to this paper: More
...the labels of individual examples, asks questions relating to statistics about the examples. These statistical query (SQ) algorithms [52, 8, 9, 22] can also be shown to meet the PAC criteria. This holds even in the presence of classification noise, in which each example (with...
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BibTeX entry: (Update)
J. Aslam & S. Decatur. Specification and simulation of statistical query algorithms for efficiency and noise tolerance. In Proc. of the 8th Ann. ACM Conf. on Computational Learning Theory, pp 437--446, 1995. http://citeseer.ist.psu.edu/article/aslam95specification.html More
@article{ aslam98specification,
author = "Javed A. Aslam and Scott E. Decatur",
title = "Specification and Simulation of Statistical Query Algorithms for Efficiency and Noise Tolerance",
journal = "Journal of Computer and System Sciences",
volume = "56",
number = "2",
pages = "191-208",
year = "1998",
url = "citeseer.ist.psu.edu/article/aslam95specification.html" }
Citations (may not include all citations):493 Communications of the ACM (context) - Valiant, of et al. - 1984
465 Learnability and the Vapnik-Chervonenkis dimension (context) - Blumer, Ehrenfeucht et al. - 1989 ACM DBLP
273 The strength of weak learnability - Schapire - 1990 ACM DBLP
268 Decision theoretic generalizations of the PAC model for neur.. (context) - Haussler - 1992 ACM DBLP
180 Boosting a weak learning algorithm by majority - Freund - 1990 ACM DBLP
151 A general lower bound on the number of examples needed for l.. (context) - Ehrenfeucht, Haussler et al. - 1989
107 Efficient noise-tolerant learning from statistical queries - Kearns - 1993 ACM DBLP
94 Learning in the presence of malicious errors - Kearns, Li - 1988 ACM DBLP
63 Weakly learning DNF and characterizing statistical query lea.. - Blum, Furst et al. - 1994 ACM DBLP
52 Learning from Good and Bad Data (context) - Laird - 1988 ACM
37 Statistical queries and faulty PAC oracles - Decatur - 1993 ACM DBLP
37 An improved boosting algorithm and its implications on learn.. (context) - Freund - 1992 ACM DBLP
33 General bounds on statistical query learning and PAC learnin.. - Aslam, Decatur - 1993 ACM DBLP
11 On learning from noisy and incomplete examples - Decatur, Gennaro - 1995 ACM DBLP
10 the sample complexity of noise-tolerant learning - Aslam, Decatur - 1996
7 Learning in hybrid noise environments using statistical quer.. - Decatur - 1995
6 Rates of uniform almost-sure convergence for empirical proce.. (context) - Pollard - 1986
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