Face detection by aggregated Bayesian network classifiers
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth 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
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Economical active feature-value acquisition through Expected Utility estimation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Maximizing classifier utility when training data is costly
ACM SIGKDD Explorations Newsletter
Optimizing estimated loss reduction for active sampling in rank learning
Proceedings of the 25th international conference on Machine learning
Decision-Centric Active Learning of Binary-Outcome Models
Information Systems Research
Cost-minimising strategies for data labelling: optimal stopping and active learning
FoIKS'08 Proceedings of the 5th international conference on Foundations of information and knowledge systems
Efficiently learning the accuracy of labeling sources for selective sampling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proactive learning for building machine translation systems for minority languages
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Towards maximizing the accuracy of human-labeled sensor data
Proceedings of the 15th international conference on Intelligent user interfaces
Active learning for biomedical citation screening
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Regression Learning with Multiple Noisy Oracles
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Bringing active learning to life
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Modeling annotation time to reduce workload in comparative effectiveness reviews
Proceedings of the 1st ACM International Health Informatics Symposium
Learning to ask the right questions to help a learner learn
Proceedings of the 16th international conference on Intelligent user interfaces
A comparison of models for cost-sensitive active learning
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Modeling users of intelligent systems
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Using decision-theoretic experience sampling to build personalized mobile phone interruption models
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Deploying an interactive machine learning system in an evidence-based practice center: abstrackr
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Active learning with Amazon Mechanical Turk
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Is Someone in this Office Available to Help Me?
Journal of Intelligent and Robotic Systems
Leveraging matching dependencies for guided user feedback in linked data applications
Proceedings of the Ninth International Workshop on Information Integration on the Web
Multi-domain active learning for text classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning for hierarchical text classification
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Towards anytime active learning: interrupting experts to reduce annotation costs
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
Selective sampling and active learning from single and multiple teachers
The Journal of Machine Learning Research
Visual tracking via weakly supervised learning from multiple imperfect oracles
Pattern Recognition
Repeated labeling using multiple noisy labelers
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Proactive learning is a generalization of active learning designed to relax unrealistic assumptions and thereby reach practical applications. Active learning seeks to select the most informative unlabeled instances and ask an omniscient oracle for their labels, so as to retrain the learning algorithm maximizing accuracy. However, the oracle is assumed to be infallible (never wrong), indefatigable (always answers), individual (only one oracle), and insensitive to costs (always free or always charges the same). Proactive learning relaxes all four of these assumptions, relying on a decision-theoretic approach to jointly select the optimal oracle and instance, by casting the problem as a utility optimization problem subject to a budget constraint. Results on multi-oracle optimization over several data sets demonstrate the superiority of our approach over the single-imperfect-oracle baselines in most cases.