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
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Boosting with incomplete information
Proceedings of the 25th international conference on Machine learning
Multi-View Face Detection Based on AdaBoost and Skin Color
ICINIS '08 Proceedings of the 2008 First International Conference on Intelligent Networks and Intelligent Systems
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Creating an ensemble of diverse support vector machines using adaboost
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Multiple-view multiple-learner active learning
Pattern Recognition
Fast rotation invariant multi-view face detection based on real adaboost
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Multi-source transfer learning with multi-view adaboost
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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Adaboost, one of the most famous boosting algorithms, has been used in various fields of machine learning. With its success, many people focus on the improvement of this algorithm in different ways. In this paper, we propose a new algorithm to improve the performance of adaboost by the theory of multi-view learning, which is called Embedded Multi-view Adaboost (EMV-Adaboost). Different from some approaches used by other researchers, we not only blend multi-view learning into adaboost thoroughly, but also output the final hypothesis in a new form of the combination of multi-learners. These theories are combined into a whole in this paper. Furthermore, we analyze the effectiveness and feasibility of EMV-Adaboost. Experimental results with our algorithm validate its effectiveness.