Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth 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
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Active learning for logistic regression: an evaluation
Machine Learning
Learning on the border: active learning in imbalanced data classification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A stopping criterion for active learning
Computer Speech and Language
ECML '07 Proceedings of the 18th European conference on Machine Learning
Exploiting multiple classifier types with active learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Reducing class imbalance during active learning for named entity annotation
Proceedings of the fifth international conference on Knowledge capture
Stopping criteria for active learning of named entity recognition
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Multi-criteria-based strategy to stop active learning for data annotation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Representative sampling for text classification using support vector machines
ECIR'03 Proceedings of the 25th European conference on IR research
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A unified approach to active dual supervision for labeling features and examples
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
On cross-validation and stacking: building seemingly predictive models on random data
ACM SIGKDD Explorations Newsletter
Online active inference and learning
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Deploying an interactive machine learning system in an evidence-based practice center: abstrackr
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
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Despite the tremendous level of adoption of machine learning techniques in real-world settings, and the large volume of research on active learning, active learning techniques have been slow to gain substantial traction in practical applications. This reluctance of adoption is contrary to active learning's promise of reduced model-development costs and increased performance on a model-development budget. This essay presents several important and under-discussed challenges to using active learning well in practice. We hope this paper can serve as a call to arms for researchers in active learning--an encouragement to focus even more attention on how practitioners might actually use active learning.