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Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negative examples.
Time bounds for selection
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Michalski, S. R., Carbonell, G. J., and Mitchell, M. T · 1986
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Learning from noisy examples
Angluin, D. and Laird, P · 1988
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Multiple kernel learning from noisy labels by stochastic programming
Yang, T., Mahdavi, M., Jin, R., Zhang, L., and Zhou, Y · 1988
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Instance-based learning algorithms
Aha, D. W., Kibler, D., and Albert, M. K · 1991
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One-class classifier networks for target recognition applications
Moya, M. M., Koch, M. W., and Hostetler, L. D · 1993
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Bagging predictors
Breiman, L · 1996
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Bootstrapping with noise: An effective regularization technique
Raviv, Y. and Intrator, N · 1996
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Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T · 1998
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Model selection for support vector machines
Chapelle, O. and Vapnik, V · 1999
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Estimating support of a high dimensional distribution
Platt, J., Schölkopf, B., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C · 1999
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Understanding the behavior of co-training
Nigam, K. and Ghani, R · 2000
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One-class svms for document classification
Manevitz, L. M. and Yousef, M · 2002
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Learning with positive and unlabeled examples using weighted logistic regression
Lee, W. and Liu, B · 2003
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Building text classifiers using positive and unlabeled examples
Liu, B., Dai, Y., Li, X., Lee, W. S., and Yu, P. S · 2003
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Pruning training sets for learning of object categories
Angelova, A., Abu-Mostafam, Y., and Perona, P · 2005
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The relationship between precision-recall and roc curves
Davis, J. and Goadrich, M · 2006
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Learning classifiers from only positive and unlabeled data
Elkan, C. and Noto, K · 2008
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Predicting accurate probabilities with a ranking loss
Menon, A. K., Jiang, X., Vembu, S., Elkan, C., and Ohno-Machado, L · 2012
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Density Ratio Estimation in ML
Sugiyama, M., Suzuki, T., and Kanamori, T · 2012
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Learning with noisy labels
Natarajan, N., Dhillon, I. S., Ravikumar, P. K., and Tewari, A · 2013
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Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C., Blanchard, G., and Handy, G · 2013
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A bagging svm to learn from positive and unlabeled examples
Mordelet, F. and Vert, J. P · 2014
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A robust ensemble approach to learn from positive and unlabeled data using {SVM} base models
Claesen, M., Smet, F. D., Suykens, J. A., and Moor, B. D · 2015
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LogisticRegression Class at scikit-learn , 2016
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