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Nearest neighbor has always been one of the most appealing non-parametric approaches in machine learning, pattern recognition, computer vision, etc.
Discriminatory analysis, nonparametric discrimination
Fix, E. and Hodges, J. (1951) · 1951
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Nearest neighbor pattern classification
Cover, T. and Hart, P. (1967) · 1967
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Convergence of the nearest neighbor rule
Wagner, T. (1971) · 1971
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Consistent nonparametric regression
Stone, C. (1977) · 1977
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On the inequality of cover and hart in nearest neighbor discrimination
Devroye, L. (1981) · 1981
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Learning from noisy examples
Angluin, D. and Laird, P. (1988) · 1988
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Efficient noise-tolerant learning from statistical queries
Kearns, M. (1993) · 1993
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Learning linear threshold functions in the presence of classification noise
Bylander, T. (1994) · 1994
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On the strong universal consistency of nearest neighbor regression function estimates
Devroye, L., Gyorfi, L., Krzyzak, A., and Lugosi, G. (1994) · 1994
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Rates of convergence of nearest neighbor estimation under arbitrary sampling
Kulkarni, S. and Posner, S. (1995) · 1995
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On the sample complexity of noise-tolerant learning
Aslam, J. and Decatur, S. (1996) · 1996
Earlier work this paper cites.
A Probabilistic Theory of Pattern Recognition
Devroye, L., Györfi, L., and Lugosi, G. (1996) · 1996
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Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T. (1998) · 1998
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Identifying mislabeled training data
Brodley, C. and Friedl, M. (1999) · 1999
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Sample-efficient strategies for learning in the presence of noise
Cesa-Bianchi, N., Dichterman, E., Fischer, P., Shamir, E., and Simon, H. (1999) · 1999
Earlier work this paper cites.
Decontamination of training samples for supervised pattern recognition methods
Barandela, R. and Gasca, E. (2000) · 2000
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Estimating a kernel fisher discriminant in the presence of label noise
Lawrence, N. and Schölkopf, B. (2001) · 2001
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Optimal aggregation of classifiers in statistical learning
Tsybakov, A. (2004) · 2004
Cited alongside, same era.
Boosting in the presence of noise
Kalai, A. and Servediob, R. (2005) · 2005
Cited alongside, same era.
Cover trees for nearest neighbor
Beygelzimer, A., Kakade, S., and Langford, J. (2006) · 2006
Cited alongside, same era.
Online passive-aggressive algorithms
Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., and Singer, Y. (2006) · 2006
Cited alongside, same era.
Robust support vector machine training via convex outlier ablation
Xu, L., Crammer, K., and Schuurmans, D. (2006) · 2006
Cited alongside, same era.
Class noise mitigation through instance weighting
Rebbapragada, U. and Brodley, C. (2007) · 2007
Cited alongside, same era.
Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C., Blanchard, G., and Handy, G. (2013) · 2013
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Stochastic k k -neighborhood selection for supervised and unsupervised learning
Tarlow, D., Swersky, K., Swersky, K., Charlin, L., Sutskever, I., and Zemel, R. (2013) · 2013
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Rates of convergence for nearest neighbor classification
Chaudhuri, K. and Dasgupta, S. (2014) · 2014
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Classification in the presence of label noise: a survey
Frenay, B. and Verleysen, M. (2014) · 2014
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Maximum margin multiclass nearest neighbors
Kontorovich, A. and Weiss, R. (2014) · 2014
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Stochastic neighbor compression
Kusner, M., Tyree, S., Weinberger, K., and Agrawal, K. (2014) · 2014
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Confidence-weighted linear classification
Dredze, M., Crammer, K., and Pereira, F. (2008) · 2008
Cited alongside, same era.
Agnostic online learning
Ben-David, S., Pál, D., and Shalev-Shwartz, S. (2009) · 2009
Cited alongside, same era.
Adaptive regularization of weight vectors
Crammer, K., Kulesza, A., and Dredze, M. (2009) · 2009
Cited alongside, same era.
On the design of loss functions for classification: Theory, robustness to outliers, and savageboost
Masnadi-Shirazi, H. and Vasconcelos, N. (2009) · 2009
Cited alongside, same era.
Learning via gaussian herding
Crammer, K. and Lee, D. (2010) · 2010
Cited alongside, same era.
Random classification noise defeats all convex potential boosters
Long, P. and Servedio, R. (2010) · 2010
Cited alongside, same era.
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S. and Ben-David, S. (2014) · 2014
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Active nearest neighbors in changing environments
Berlind, C. and Urner, R. (2015) · 2015
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Lectures on the Nearest Neighbor Method
Biau, G. and Devroye, L. (2015) · 2015
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A bayes consistent 1-nn classifier
Kontorovich, A. and Weiss, R. (2015) · 2015
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Learning from corrupted binary labels via class-probability estimation
Menon, A., Rooyen, B., Ong, C., and Williamson, B. (2015) · 2015
Later among the works it cites.
Risk minimization in the presence of label noise
Gao, W., Wang, L., Li, Y.-F., and Zhou, Z.-H. (2016) · 2016
Closest in time.
Active nearest-neighbor learning in metric spaces
Kontorovich, A., Sabato, S., and Urner, R. (2016) · 2016
Closest in time.
Classification with noisy labels by importance reweighting
Liu, T. and Tao, D. (2016) · 2016
Closest in time.
A simple two-sample Bayesian t-test for hypothesis testing
Wang, M. and Liu, G. (2016) · 2016
Closest in time.
Nearest-neighbor sample compression: Efficiency, consistency, infinite dimensions
Kontorovich, A., Sabato, S., and Weiss, R. (2017) · 2017
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Mixture proportion estimation via kernel embeddings of distributions
Ramaswamy, H., Scott, C., and Tewari, A. (2016) · 2060
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