COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Active learning using adaptive resampling
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Active learning for automatic classification of software behavior
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Convergence and Application of Online Active Sampling Using Orthogonal Pillar Vectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic scheduling of active support vector learning algorithms
Proceedings of the 2005 ACM symposium on Applied computing
Active Learning to Recognize Multiple Types of Plankton
The Journal of Machine Learning Research
Repairing self-confident active-transductive learners using systematic exploration
Pattern Recognition Letters
Active learning for object classification: from exploration to exploitation
Data Mining and Knowledge Discovery
Support kernel machine-based active learning to find labels and a proper kernel simultaneously
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Active learning for biomedical citation screening
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Coached active learning for interactive video search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Adaptive active classification of cell assay images
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
EGAL: exploration guided active learning for TCBR
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Feedback-driven multiclass active learning for data streams
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies.We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.