Active learning from positive and unlabeled examples
Learning from positive and unlabeled data (PU learning) is a weakly supervised variant of binary classification in which the learner receives labels only for (some) positively labeled instances, while all other examples remain unlabeled. Motivated by applications such as advertising and anomaly detection, we study an active PU learning setting where the learner can adaptively query instances from an unlabeled pool, but a queried label is revealed only when the instance is positive and an independent coin flip succeeds; otherwise the learner receives no information. In this paper, we provide the first theoretical analysis of the label complexity of active PU learning.
Code (0)
등록된 구현이 없습니다.
Tasks
Binary ClassificationAnomaly DetectionActive LearningSimilar Papers 제목 키워드 기반
Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification
Motivated by applications in protein function prediction, we consider a challenging supervised classification setting in which positive labels are scarce and there are no explicit negative labels. The learning algorithm …
Active LearningGeneral ClassificationPredictionProtein Function PredictionActive Covering
We analyze the problem of active covering, where the learner is given an unlabeled dataset and can sequentially label query examples. The objective is to label query all of the positive examples in the fewest number of t…
Active LearningPositive-Unlabeled Compression on the Cloud
Many attempts have been done to extend the great success of convolutional neural networks (CNNs) achieved on high-end GPU servers to portable devices such as smart phones. Providing compression and acceleration service o…
GPUKnowledge DistillationD-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation
Positive Unlabeled (PU) learning consists in learning to distinguish samples of our class of interest, the positive class, from the counter-examples, the negative class, by using positive labeled and unlabeled samples du…
Generative Adversarial NetworkLearning from positive and unlabeled data: a survey
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and neg…
BIG-bench Machine LearningKnowledge Base CompletionMedical DiagnosisSurvey