Information-based objective functions for active data selection
Neural Computation
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
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
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Novel reconfigurable computing architectures for embedded high performance signal processing and numerical applications
User Modeling and User-Adapted Interaction
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Pay-as-you-go user feedback for dataspace systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Principles of Lifelong Learning for Predictive User Modeling
UM '07 Proceedings of the 11th international conference on User Modeling
Estimating annotation cost for active learning in a multi-annotator environment
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
Optimal value of information in graphical models
Journal of Artificial Intelligence Research
Actively Learning Ontology Matching via User Interaction
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Bucking the trend: large-scale cost-focused active learning for statistical machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Cost-Sensitive Active Visual Category Learning
International Journal of Computer Vision
A comparison of models for cost-sensitive active learning
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A utility-theoretic approach to privacy in online services
Journal of Artificial Intelligence Research
Proceedings of the VLDB Endowment
Active learning to maximize accuracy vs. effort in interactive information retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Asking generalized queries with minimum cost
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Video Behaviour Mining Using a Dynamic Topic Model
International Journal of Computer Vision
A unifying theory of active discovery and learning
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
Major life changes and behavioral markers in social media: case of childbirth
Proceedings of the 2013 conference on Computer supported cooperative work
Predicting postpartum changes in emotion and behavior via social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Towards anytime active learning: interrupting experts to reduce annotation costs
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
Active learning from relative queries
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Collaborative information acquisition for data-driven decisions
Machine Learning
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An inescapable bottleneck with learning from large data sets is the high cost of labeling training data. Unsupervised learning methods have promised to lower the cost of tagging by leveraging notions of similarity among data points to assign tags. However, unsupervised and semi-supervised learning techniques often provide poor results due to errors in estimation. We look at methods that guide the allocation of human effort for labeling data so as to get the greatest boosts in discriminatory power with increasing amounts of work. We focus on the application of value of information to Gaussian Process classifiers and explore the effectiveness of the method on the task of classifying voice messages.