Javed A. Aslam, Scott E. Decatur  Home/Search
Context Related
Links: ACM DBLP | dartmouth.edu/~jaa/p...AslamDe98b.ps.gz Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: dartmouth.edu/~jaa/papers...index (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
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...
Cited by: More
Decision Trees: More Theoretical Justification for Practical.. - Fiat, Pechyony (Correct)
Efficient Noise-Tolerant Learning From Statistical Queries - Kearns (1998) (Correct)
Decision Trees: More Theoretical Justification - For Practical Algorithms (Correct)
Similar documents (at the sentence level): More
39.9%: Specification and Simulation of Statistical Query Algorithms.. - Javed Aslam (1995) (Correct)
26.6%: Efficient Learning from Faulty Data - Decatur (1995) (Correct)
13.9%: Improved Noise-Tolerant Learning and Generalized Statistical.. - Aslam, Decatur (1994) (Correct)
Active bibliography (related documents): More All
0.3: PAC Learning with Nasty Noise - Bshouty, Eiron, Kushilevitz (1999) (Correct)
0.2: Learning with Limited Visibility - Dichterman (1998) (Correct)
0.2: Noise-tolerant learning, the parity problem, and the.. - Blum, Kalai, Wasserman (2003) (Correct)
Similar documents based on text: More All
0.3: Noise Tolerant Algorithms for Learning and Searching - Aslam (1995) (Correct)
0.3: Learning with Queries Corrupted by Classification Noise - Jackson, Shamir, Shwartzman (1999) (Correct)
0.2: New Lower Bounds for Statistical Query Learning - Ke Yang April (2002) (Correct)
Related documents from co-citation: More All
12: Efficient noise-tolerant learning from statistical queries - Kearns - 1993
10: Statistical queries and faulty PAC oracles - Decatur - 1993
10: Learnability and the Vapnik Chervonenkis Dimension (context) - Blumer, Ehrenfeucht et al. - 1989
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/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/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
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 ACM DBLP
149 Quantifying inductive bias: AI learning algorithms and Valia.. (context) - Haussler - 1988
132 Fast probabilistic algorithms for Hamiltonian circuits and m.. (context) - Angluin, Valiant - 1979 ACM DBLP
107 Efficient noise-tolerant learning from statistical queries - Kearns - 1993 ACM DBLP
94 Learning in the presence of malicious errors - Kearns, Li - 1993
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 An improved boosting algorithm and its implications on learn.. (context) - Freund - 1992 ACM DBLP
37 Statistical queries and faulty PAC oracles - Decatur - 1993 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
6 Rates of uniform almost-sure convergence for empirical proce.. (context) - Pollard - 1986
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.cs.dartmouth.edu/~jaa/papers/index.html):
General Bounds on Statistical Query Learning and PAC Learning .. - Aslam, Decatur (1993) (Correct)
Noise Tolerant Algorithms for Learning and Searching - Aslam (1995) (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC