Smart PAC-learners

Theoretical Computer Science

Volumn 412, Issue 19, 2011, Pages 1756-1766

  Darnstädt, Malte ^a   Simon, Hans Ulrich ^a  

^a RUHR UNIVERSITY BOCHUM   (Germany)

Author keywords

Learning under a fixed distribution; Machine learning; Minimax theorem; PAC learning; Semi supervised learning; Smart PAC learner; Value of unlabeled data

Indexed keywords

LEARNING UNDER A FIXED DISTRIBUTION; MACHINE LEARNING; MINIMAX THEOREM; PAC LEARNING; SEMI-SUPERVISED LEARNING; SMART PAC-LEARNER; VALUE OF UNLABELED DATA;

ERROR ANALYSIS; INFORMATION THEORY; SUPERVISED LEARNING;

THEOREM PROVING;

EID: 79952619803     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tcs.2010.12.053     Document Type: Article

References (15)

1. Maria-Florina Balcan, Alina Beygelzimer, and John Langford Agnostic active learning Journal of Computer and System Sciences 75 1 2009 78 89
2. Maria-Florina Balcan, and Avrim Blum A discriminative model for semi-supervised learning Journal of the Association on Computing Machinery 57 3 2010 19:1 19:46
3. Maria-Florina Balcan, Andrei Broder, Tong Zhang, Margin based active learning, in: Proceedings of the 20th Annual Conference on Learning Theory, 2007, pp. 3550.
4. Maria-Florina Balcan, Steve Hanneke, and Jennifer Wortman Vaughan The true sample complexity of active lerning Machine Learning 80 23 2010 111 139
5. Shai Ben-David, Tyler Lu, Dvid Pl, Does unlabeled data provably help? Worst-case analysis of the sample complexity of semi-supervised learning, in: Proceedings of the 21st Annual Conference on Learning Theory, 2008, pp. 3344.
6. Gyora M. Benedek, and Alon Itai Learnability with respect to fixed distributions Theoretical Computer Science 86 2 1991 377 389 (Pubitemid 21692478)
7. Avrim Blum, Tom Mitchell, Combining labeled and unlabeled data with co-training, in: Proceedings of the 11th Annual Conference on Computational Learning Theory, 1998, pp. 92100.
8. Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K. Warmuth Learnability and the VapnikChervonenkis dimension Journal of the Association on Computing Machinery 36 4 1989 929 965
9. Olivier Chapelle, Bernhard Schlkopf, and Alexander Zien Semi-Supervised Learning 2006 MIT Press
10. David Cohn, Les Atlas, and Richard Ladner Improving generalization with active learning Machine Learning 15 3 1994 201 221
11. Sanjoy Dasgupta Coarse sample complexity bounds for active learning Advances in Neural Information Processing Systems 18 2006 MIT Press 235 242
12. Sanjoy Dasgupta, Daniel Hsu, and Claire Monteleoni A general agnostic active learning algorithm Advances in Neural Information Processing Systems 20 2008 MIT Press 353 360
13. Andrzej Ehrenfeucht, David Haussler, Michael Kearns, and Leslie Valiant A general lower bound on the number of examples needed for learning Information and Computation 82 3 1989 247 261 (Pubitemid 20597150)
14. Steve Hanneke, A bound on the label complexity of active learning, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 353360.
15. Peter Morris Introduction to Game Theory 1994 Springer