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In this paper we explore noise tolerant learning of classifiers.
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S. Fine, R. Gilad-bachrach, S. Mendelson, and N. Tishby, “Noise tolerant learnability via the dual learning problem,” in NSCT
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Wiley-Interscience, 2000
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (2nd Edition) · 2000
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Boston, MA: Kluwar, 2004
M. Thathachar and P. Sastry, Networks of Learning Automata: Techniques for Online Stochastic Optimization · 2004
Cited alongside, same era.
Q. Tao, G. Wu, F. Wang, and J. Wang, “Posterior probability support vector machines for unbalanced data,” IEEE Transactions in Neural Networks
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Cited alongside, same era.
Springer-Verlag New York, Inc., 2006
C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) · 2006
Cited alongside, same era.
P. L. Bartlett and M. I. Jordan, “Convexity, classification, and risk bounds,” Journal of The American Statistical Association
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Cited alongside, same era.
L. Xu, K. Crammer, and D. Schuurmans, “Robust support vector machine training via convex outlier ablation,” in Proceedings of the 21st National Conference on Artificial Intelligence (AAAI-06)
2006
Later among the works it cites.
G. Stempfel, L. Ralaivola, and F. Denis, “Learning from noisy data using hyperplane sampling and sample averages,” Technical Report 3564, HAL - CNRS, France, 2007
2007
Later among the works it cites.
P. S. Sastry, G. D. Nagendra, and N. Manwani, “A continuous-action learning automata team for noise tolerant learning of half spaces,” IEEE Transaction on System, Man and Cybernetics Part B: Cybernetics
2010
Later among the works it cites.
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