Shai Fine, Ran Gilad-Bachrach, Eli Shamir, Naftali Tishby  Home/Search
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Abstract: Generalization in most PAC learning analysis starts around O (d) examples, where d = V C dim of the class. Nevertheless, analysis of learning curves using statistical mechanics shows much earlier generalization [7]. Here we introduce a gadget called Early Predictor, which exists if somewhat better than random prediction of the label of an arbitrary instance can be obtained from labels of O (log d) random examples. We were able to show that by taking a majority vote over a committee of Early... (Update)
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...hence, this assumption may be regarded as questionable. However, in some cases, the MQ oracle can be e ciently simulated to meet our needs [8]. Throughout this paper we denote by the sample space and by H the hypotheses class. We endow H with a known probability measure p, and...
...hence, this assumption may be regarded as questionable. However, in some cases, the MQ oracle can be eciently simulated to meet our needs [52]. Throughout this chapter we denote by the sample space and by H the hypotheses class. We endow H with a known probability measure p, and...
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BibTeX entry: (Update)
S. Fine, R. Gilad-Bachrach, E. Shamir, and N. Tishby. Noise tolerant learning using early predictors. subbmitted to NIPS 1999. http://citeseer.ist.psu.edu/fine99noise.html More
@misc{ fine99noise,
author = "S. Fine and R. Gilad-Bachrach and E. Shamir and N. Tishby",
title = "Noise tolerant learning using early predictors",
text = "S. Fine, R. Gilad-Bachrach, E. Shamir, and N. Tishby. Noise tolerant learning
using early predictors. subbmitted to NIPS 1999.",
year = "1999",
url = "citeseer.ist.psu.edu/fine99noise.html" }
Citations (may not include all citations):657 Bagging predictors - Breiman - 1996
509 A decision-theoretic generalization of on-line learning and .. - Freund, Schapire - 1997
243 Boosting in the margin: a new explanation for the effectiven.. - Schapire, Freund et al. - 1998
142 Learning from noisy examples (context) - Angluin, Laird - 1988
134 An experimental comparison of three methods for constructing.. - Dietterich - 1998
107 Efficient noise-tolerant learning from statistical queries - Kearns - 1993
81 Support vector method for function approximation - Vapnik, Golowich et al. - 1996
47 Rigorous learning curve bounds from statistical mechanics - Haussler, Kearns et al. - 1997
20 Hardness results for neural network approximation problems - Bartlett, Ben-David - 1999
15 Journal of Computer and System Sciences (context) - Helmbold, Warmuth et al. - 1995
3 Learning with queries corrupted by classification noise - Jackson, Shamir et al. - 1999
2 Unpublished manuscript (context) - Gofer, Shamir - 1998
1 Efficient Learning from Faulty Data (context) - Decator - 1995
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