Publications related to An Improved Predictive Accuracy Bound for Averaging Classifiers

Online Policy Adaptation for Ensemble Classifiers

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

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 ...

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 ...

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

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 Algorithms

Samy Bengio, Christos Dimitrakakis

Ensemble algorithms are general methods for improving the performance of a given learning algorithm. This is achieved by the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and com ...

IDIAP2002

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

An Improved Predictive Accuracy Bound for Averaging Classifiers

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Online Policy Adaptation for Ensemble Classifiers

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

Boosting HMMs with an application to speech recognition

Boosting HMMs with an application to speech recognition

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

Online Policy Adaptation for Ensemble Algorithms

Mixture models for unsupervised and supervised learning

Boosting HMMs with an application to speech recognition

Online Policy Adaptation for Ensemble Classifiers

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

Boosting HMMs with an application to speech recognition

Boosting HMMs with an application to speech recognition

Mixture models for unsupervised and supervised learning

Online Policy Adaptation for Ensemble Classifiers

Online Policy Adaptation for Ensemble Classifiers

Online Policy Adaptation for Ensemble Classifiers

Online Policy Adaptation for Ensemble Algorithms