COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Machine Learning
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth 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
Clustering documents into a web directory for bootstrapping a supervised classification
Data & Knowledge Engineering - Special issue: WIDM 2003
ICML '06 Proceedings of the 23rd international conference on Machine learning
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A bound on the label complexity of agnostic active learning
Proceedings of the 24th international conference on Machine learning
Hierarchical sampling for active learning
Proceedings of the 25th international conference on Machine learning
Active learning with multiple views
Journal of Artificial Intelligence Research
Active learning with statistical models
Journal of Artificial Intelligence Research
Active learning in the non-realizable case
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Semi-automatic creation and maintenance of web resources with webtopic
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
d-Confidence: an active learning strategy which efficiently identifies small classes
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
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Collecting and annotating exemplary cases is a costly and critical task that is required in early stages of any classification process. Reducing labeling cost without degrading accuracy calls for a compromise solution which may be achieved with active learning. Common active learning approaches focus on accuracy and assume the availability of a pre-labeled set of exemplary cases covering all classes to learn. This assumption does not necessarily hold. In this paper we study the capabilities of a new active learning approach, d-Confidence, in rapidly covering the case space when compared to the traditional active learning confidence criterion, when the representativeness assumption is not met. Experimental results also show that d-Confidence reduces the number of queries required to achieve complete class coverage and tends to improve or maintain classification error.