Original Contribution: Stacked generalization
Neural Networks
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Prediction, Learning, and Games
Prediction, Learning, and Games
Machine Learning
On multi-view active learning and the combination with semi-supervised learning
Proceedings of the 25th international conference on Machine learning
Exploration-exploitation tradeoff using variance estimates in multi-armed bandits
Theoretical Computer Science
Bandit-based optimization on graphs with application to library performance tuning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Extreme compass and dynamic multi-armed bandits for adaptive operator selection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Algorithms combining multi-view information are known to exponentially quicken classification, and have been applied to many fields. However, they lack the ability to mine most discriminant information sources (or data types) for making predictions. In this paper, we propose an algorithm based on boosting to address these problems. The proposed algorithm builds base classifiers independently from each data type (view) that provides a partial view about an object of interest. Different from AdaBoost, where each view has its own re-sampling weight, our algorithm uses a single re-sampling distribution for all views at each boosting round. This distribution is determined by the view whose training error is minimal. This shared sampling mechanism restricts noise to individual views, thereby reducing sensitivity to noise. Furthermore, in order to establish performance guarantees, we introduce a randomized version of the algorithm, where a winning view is chosen probabilistically. As a result, it can be cast within a multi-armed bandit framework, which allows us to show that with high probability the algorithm seeks out most discriminant views of data for making predictions. We provide experimental results that show its performance against noise and competing techniques.