A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
Tutorial on Practical Prediction Theory for Classification
The Journal of Machine Learning Research
Learning with Decision Lists of Data-Dependent Features
The Journal of Machine Learning Research
Generalization error bounds using unlabeled data
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Visual-context boosting for eye detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Supervised learning is concerned with the task of building accurate classifiers from a set of labelled examples. However, the task of gathering a large set of labelled examples can be costly and time-consuming. Active learning algorithms try to reduce this labelling cost by performing a small number of label-queries from a large set of unla-belled examples during the process of building a classifier. However, the level of performance achieved by active learning algorithms is not always up to our expectations and no rigorous performance guarantee, in the form of a risk bound, exists for non-trivial active learning algorithms. In this paper, we propose a novel (and easy to implement) active learning algorithm having a rigorous performance guarantee (i.e., a valid risk bound) and that performs very well in comparison with some widely-used active learning algorithms.