unlabeled example
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Semi-Supervised Learning with Declaratively Specified Entropy Constraints
Haitian Sun, William W. Cohen, Lidong Bing
Neural Information Processing Systems http://nips.cc/
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MarginGAN: Adversarial Training in Semi-Supervised Learning
The newfeature isthattheclassifier attempts toincrease themarginofrealexamples and to decrease the margin of fake examples.
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MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin A. Raffel
Neural Information Processing Systems http://nips.cc/
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A Rob
Ifnosuch(mu,ml) exist, thenMAG( , , ,H,U, )= ;.
47b4f1bfdf6d298682e610ad74b37dca-Paper.pdf
Given only positive examples and unlabeled examples (from both positive and negative classes), we might hope nevertheless to estimate an accurate positiveversus-negative classifier. Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation(MPE)--determining the fraction of positive examples in the unlabeled data; and (ii)PU-learning--given such an estimate, learning the desired positive-versus-negative classifier.