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Online Policy Adaptation for Ensemble Algorithms

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Towards a Robust BCI: Error Potentials and Online Learning

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Recent advances in the field of Brain-Computer Interfaces (BCIs) have shown that BCIs have the potential to provide a powerful new channel of communication, completely independent of muscular and nervous systems. However, while there have been successful l ...

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Online Policy Adaptation for Ensemble Classifiers

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Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper we attempt to train and combine the base classifiers using an adaptive policy. This policy is ...

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IDIAP2005

Online Policy Adaptation for Ensemble Classifiers

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Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...

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Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...

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Boosting HMMs with an application to speech recognition

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Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

IDIAP2003

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

IDIAP2003

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

IDIAP2003

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