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In the problem of domain adaptation for binary classification, the learner is presented with labeled examples from a source domain, and must correctly classify unlabeled examples from a target domain, which may differ from the source.
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Adjusting the outputs of a classifier to new a priori probabilities may significantly improve classification accuracy: Evidence from a multi-class problem in remote sensing
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John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman · 2008
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Direct importance estimation for covariate shift adaptation
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Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Making risk minimization tolerant to label noise
Aritra Ghosh, Naresh Manwani, and P.S. Sastry · 2015
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Learning from corrupted binary labels via class-probability estimation
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A new pac-bayesian perspective on domain adaptation
Pascal Germain, Amaury Habrard, François Laviolette, and Emilie Morvant · 2016
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Surrogate regret bounds for generalized classification performance metrics
Wojciech Kotlowski and Krzysztof Dembczyński · 2016
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On the hardness of domain adaptation and the utility of unlabeled target samples
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Accuracy at the top
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Semi-supervised learning of class balance under class-prior change by distribution matching
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Learning from weak teachers
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A survey on Neyman-Pearson classification and suggestions for future research
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Learning with bounded instance- and label-dependent label noise
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Consistency analysis for binary classification revisited
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Learning with confident examples: Rank pruning for robust classification with noisy labels
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Fisher consistency for prior probability shift
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Classification with imperfect training labels
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Learning from binary labels with instance-dependent noise
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