A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Information-based objective functions for active data selection
Neural Computation
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active Hidden Markov Models for Information Extraction
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Unsupervised active learning in large domains
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Algorithms for optimal scheduling and management of hidden Markovmodel sensors
IEEE Transactions on Signal Processing
Corrective feedback and persistent learning for information extraction
Artificial Intelligence
Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Asset priority risk assessment using hidden markov models
Proceedings of the 10th ACM conference on SIG-information technology education
Active learning for part-of-speech tagging: accelerating corpus annotation
LAW '07 Proceedings of the Linguistic Annotation Workshop
Corrective feedback and persistent learning for information extraction
Artificial Intelligence
Intelligent acquisition and learning of fluorescence microscope data models
IEEE Transactions on Image Processing
A statistical reasoning system for medication prompting
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Margin-Based active learning for structured output spaces
ECML'06 Proceedings of the 17th European conference on Machine Learning
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by closing the data-gathering loop, i.e., they choose the examples most informative with respect to their learning objectives. We introduce a framework and objective functions for active learning in three fundamental HMM problems: model learning, state estimation, and path estimation. In addition, we describe a new set of algorithms for efficiently finding optimal greedy queries using these objective functions. The algorithms are fast, i.e., linear in the number of time steps to select the optimal query and we present empirical results showing that these algorithms can significantly reduce the need for labelled training data.