Publications related to Online Policy Adaptation for Ensemble Algorithms

Towards a Robust BCI: Error Potentials and Online Learning

José del Rocio Millán Ruiz, Anna Buttfield, Pierre Ferrez

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 ...

2006

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

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 ...

2005

Towards Explaining the Success (Or Failure) of Fusion in Biometric Authentication

Samy Bengio, Norman Hoon Thian Poh

Biometric authentication is a process of verifying an identity claim using a person's behavioral and physiological characteristics. Due to vulnerability of the system to environmental noise and variation caused by the user, fusion of several biometric-enab ...

IDIAP2005

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

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 ...

2004

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

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 ...

2004

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

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 ...

2004

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

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 ...

IDIAP2003

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

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 ...

IDIAP2003

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

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 ...

IDIAP2003

An Improved Predictive Accuracy Bound for Averaging Classifiers

Matthias Seeger

We present an improved bound on the difference between training and test errors for voting classifiers. This improved averaging bound provides a theoretical justification for popular averaging techniques such as Bayesian classification, Maximum Entropy dis ...

2001

Online Policy Adaptation for Ensemble Algorithms

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Towards a Robust BCI: Error Potentials and Online Learning

Online Policy Adaptation for Ensemble Classifiers

Towards Explaining the Success (Or Failure) of Fusion in Biometric Authentication

Online Policy Adaptation for Ensemble Classifiers

Boosting HMMs with an application to speech recognition

Boosting HMMs with an application to speech recognition

Online Policy Adaptation for Ensemble Classifiers

Online Policy Adaptation for Ensemble Classifiers

Boosting HMMs with an application to speech recognition

An Improved Predictive Accuracy Bound for Averaging Classifiers

Towards a Robust BCI: Error Potentials and Online Learning

Online Policy Adaptation for Ensemble Classifiers

Boosting HMMs with an application to speech recognition

Online Policy Adaptation for Ensemble Classifiers

Online Policy Adaptation for Ensemble Classifiers

Boosting HMMs with an application to speech recognition

Boosting HMMs with an application to speech recognition

Online Policy Adaptation for Ensemble Classifiers

An Improved Predictive Accuracy Bound for Averaging Classifiers

Towards Explaining the Success (Or Failure) of Fusion in Biometric Authentication