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
Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Active learning using adaptive resampling
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximum likelihood estimation for filtering thresholds
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Retrieval
Machine Learning
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
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
Less is More: Active Learning with Support Vector Machines
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
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Hierarchical sampling for active learning
Proceedings of the 25th international conference on Machine learning
On proper unit selection in active learning: co-selection effects for named entity recognition
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
An intrinsic stopping criterion for committee-based active learning
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Stopping criteria for active learning of named entity recognition
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Active learning for biomedical citation screening
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Theoretical Computer Science
Good seed makes a good crop: accelerating active learning using language modeling
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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In practical classification, there is often a mix of learnable and unlearnable classes and only a classifier above a minimum performance threshold can be deployed. This problem is exacerbated if the training set is created by active learning. The bias of actively learned training sets makes it hard to determine whether a class has been learned. We give evidence that there is no general and efficient method for reducing the bias and correctly identifying classes that have been learned. However, we characterize a number of scenarios where active learning can succeed despite these difficulties.