Communications of the ACM
A new polynomial-time algorithm for linear programming
Combinatorica
Computational limitations on learning from examples
Journal of the ACM (JACM)
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Learning Nested Differences of Intersection-Closed Concept Classes
Machine Learning
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Predicting {0, 1}-functions on randomly drawn points
Information and Computation
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
An introduction to computational learning theory
An introduction to computational learning theory
Journal of Computer and System Sciences
Generalized teaching dimensions and the query complexity of learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
How many queries are needed to learn?
Journal of the ACM (JACM)
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Strong Minimax Lower Bounds for Learning
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Sampling lower bounds via information theory
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
A Probabilistic Active Support Vector Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learnability and Automatizability
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Active Learning to Recognize Multiple Types of Plankton
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
MDL convergence speed for Bernoulli sequences
Statistics and Computing
ICML '06 Proceedings of the 23rd international conference on Machine learning
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
A bound on the label complexity of agnostic active learning
Proceedings of the 24th international conference on Machine learning
Active learning for logistic regression: an evaluation
Machine Learning
Journal of Computer and System Sciences
Importance weighted active learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Analysis of Perceptron-Based Active Learning
The Journal of Machine Learning Research
Maximum margin coresets for active and noise tolerant learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Theoretical foundations of active learning
Theoretical foundations of active learning
COLT'07 Proceedings of the 20th annual conference on Learning theory
Teaching dimension and the complexity of active learning
COLT'07 Proceedings of the 20th annual conference on Learning theory
Rademacher Complexities and Bounding the Excess Risk in Active Learning
The Journal of Machine Learning Research
Smoothness, Disagreement Coefficient, and the Label Complexity of Agnostic Active Learning
The Journal of Machine Learning Research
Active learning in the non-realizable case
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Margin-Based active learning for structured output spaces
ECML'06 Proceedings of the 17th European conference on Machine Learning
Analysis of perceptron-based active learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Minimax Bounds for Active Learning
IEEE Transactions on Information Theory
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We study the theoretical advantages of active learning over passive learning. Specifically, we prove that, in noise-free classifier learning for VC classes, any passive learning algorithm can be transformed into an active learning algorithm with asymptotically strictly superior label complexity for all nontrivial target functions and distributions. We further provide a general characterization of the magnitudes of these improvements in terms of a novel generalization of the disagreement coefficient. We also extend these results to active learning in the presence of label noise, and find that even under broad classes of noise distributions, we can typically guarantee strict improvements over the known results for passive learning.