Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Optimal learning: computational procedures for bayes-adaptive markov decision processes
Optimal learning: computational procedures for bayes-adaptive markov decision processes
Active Feature-Value Acquisition for Classifier Induction
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Bayesian sparse sampling for on-line reward optimization
ICML '05 Proceedings of the 22nd international conference on Machine learning
The max K-armed bandit: a new model of exploration applied to search heuristic selection
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Learning and classifying under hard budgets
ECML'05 Proceedings of the 16th European conference on Machine Learning
Machine learning paradigms for utility-based data mining
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Decision Support Systems
Learning and classifying under hard budgets
ECML'05 Proceedings of the 16th European conference on Machine Learning
New algorithms for budgeted learning
Machine Learning
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In many practical Machine Learning tasks, there are costs associated with acquiring the feature values of training instances, as well as a hard learning budget which limits the number of feature values that can be purchased. In this budgeted learning scenario, it is important to use an effective "data acquisition policy", that specifies how to spend the budget acquiring training data to produce an accurate classifier. This paper examines a simplified version of this problem, "active model selection" [10]. As this is a Markov decision problem, we consider applying reinforcement learning (RL) techniques to learn an effective spending policy. Despite extensive training, our experiments on various versions of the problem show that the performance of RL techniques is inferior to existing, simpler spending policies.