The Strength of Weak Learnability
Machine Learning
Elements of information theory
Elements of information theory
Machine Learning
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
MadaBoost: A Modification of AdaBoost
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Exploiting unlabeled data in ensemble methods
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Wisdom of Crowds
Integration of Stochastic Models by Minimizing α-Divergence
Neural Computation
Design of an Unsupervised Weight Parameter Estimation Method in Ensemble Learning
Neural Information Processing
When Semi-supervised Learning Meets Ensemble Learning
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
SemiBoost: Boosting for Semi-Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate anomaly detection through parallelism
IEEE Network: The Magazine of Global Internetworking - Special issue title on recent developments in network intrusion detection
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Semi-Supervised Learning
Semi-supervised multiple classifier systems: background and research directions
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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When there are multiple trained predictors, one may want to integrate them into one predictor. However, this is challenging if the performances of the trained predictors are unknown and labeled data for evaluating their performances are not given. In this paper, a method is described that uses unlabeled data to estimate the weight parameters needed to build an ensemble predictor integrating multiple trained component predictors. It is readily derived from a mathematical model of ensemble learning based on a generalized mixture of probability density functions and corresponding information divergence measures. Numerical experiments demonstrated that the performance of our method is much better than that of simple average-based ensemble learning, even when the assumption placed on the performances of the component predictors does not hold exactly.