Computational geometry: an introduction
Computational geometry: an introduction
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Training connectionist networks with queries and selective sampling
Advances in neural information processing systems 2
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Strong Minimax Lower Bounds for Learning
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
An Optimal Reject Rule for Binary Classifiers
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Multiple Reject Thresholds for Improving Classification Reliability
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Support Vector Machines with Embedded Reject Option
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Tutorial on Practical Prediction Theory for Classification
The Journal of Machine Learning Research
On optimal reject rules and ROC curves
Pattern Recognition Letters
Optimizing abstaining classifiers using ROC analysis
ICML '05 Proceedings of the 22nd international conference on Machine learning
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
A bound on the label complexity of agnostic active learning
Proceedings of the 24th international conference on Machine learning
A fast linear separability test by projection of positive points on subspaces
Proceedings of the 24th international conference on Machine learning
Classification with reject option in gene expression data
Bioinformatics
Version spaces: a candidate elimination approach to rule learning
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Theoretical foundations of active learning
Theoretical foundations of active learning
The Journal of Machine Learning Research
Active learning via perfect selective classification
The Journal of Machine Learning Research
A unified view of class-selection with probabilistic classifiers
Pattern Recognition
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We consider selective classification, a term we adopt here to refer to 'classification with a reject option.' The essence in selective classification is to trade-off classifier coverage for higher accuracy. We term this trade-off the risk-coverage (RC) trade-off. Our main objective is to characterize this trade-off and to construct algorithms that can optimally or near optimally achieve the best possible trade-offs in a controlled manner. For noise-free models we present in this paper a thorough analysis of selective classification including characterizations of RC trade-offs in various interesting settings.