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Keyword

PAC learning

Description: A mathematical framework for analyzing whether machine-learning algorithms can learn concepts with high accuracy and confidence.
Wikidata ID: wiki/Q458526
OpenAlex ID: keywords/pac-learning
The Strength of Weak Learnability
1990 · Robert E. Schapire · Machine Learning · 3,429
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The strength of weak learnability
1990 · Robert E. Schapire · Machine Learning · 2,451
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Queries and Concept Learning
1988 · Dana Angluin · Machine Learning · 1,934
PDF
An Introduction to Computational Learning Theory
1994 · Michael J. Kearns, Umesh Vazirani · The MIT Press eBooks · 1,702
Boosting a Weak Learning Algorithm by Majority
1995 · Y. Freund · Information and Computation · 1,676
What Size Net Gives Valid Generalization?
1989 · Eric B. Baum, David Haussler · Neural Computation · 1,550
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm
1988 · Nick Littlestone · Machine Learning · 1,354
A brief introduction to boosting
1999 · Robert E. Schapire · 1,297
Foundations of Machine Learning
2018 · Mehryar Mohri, Afshin Rostamizadeh, et al. · The MIT Press eBooks · 1,118
Algorithmic Learning in a Random World
2005 · Vovk, Vladimir, Gammerman, Alex, et al. · Springer eBooks · 1,110
Foundations of Machine Learning
2012 · The MIT Press eBooks · 1,103
PDF
Occam's Razor
1987 · Anselm Blumer, Andrzej Ehrenfeucht, et al. · Information Processing Letters · 1,057
Wrapper induction for information extraction
1997 · Nicholas Kushmerick, Daniel S. Weld · 1,055
Inductive logic programming
1991 · Stephen Muggleton · New Generation Computing · 1,048
Cascade object detection with deformable part models
2010 · Pedro F. Felzenszwalb, Ross B. Girshick, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 928
Decision theoretic generalizations of the PAC model for neural net and other learning applications
1992 · David Haussler · Information and Computation · 843
Learning From Noisy Examples
1988 · Dana Angluin, Philip Laird · Machine Learning · 833
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Cryptographic Limitations on Learning Boolean Formulae and Finite Automata
1994 · Michael Kearns, Leslie Valiant · Journal of the ACM · 733
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Efficient Induction of Logic Programs
1990 · Stephen Muggleton, Cheng Feng · 674
Efficient noise-tolerant learning from statistical queries
1998 · Michael Kearns · Journal of the ACM · 666
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Learning with many irrelevant features
1991 · Hussein Almuallim, Thomas G. Dietterich · 619
Noise-tolerant learning, the parity problem, and the statistical query model
2003 · Avrim Blum, Adam Kalai, et al. · Journal of the ACM · 596
Constant depth circuits, Fourier transform, and learnability
1993 · Nathan Linial, Yishay Mansour, et al. · Journal of the ACM · 573
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Learning Boolean concepts in the presence of many irrelevant features
1994 · Hussein Almuallim, Thomas G. Dietterich · Artificial Intelligence · 520
Introduction to Statistical Learning Theory
2004 · Olivier Bousquet, Stéphane Boucheron, et al. · Lecture notes in computer science · 519
Works count: 2,500
Citation count: 62,880
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