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Ensembles for sequence learning

Publications associées (175)

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Statistical uncertainty analysis applied to fuel depletion calculations

Rakesh Chawla

In this paper, we present an uncertainty methodology based on a statistical approach, for assessing uncertainties in lattice code predictions of fuel composition changes with burnup due to uncertainties in the fuel geometric configuration, initial enrichme ...

Atomic Energy Society of Japan2007

Ensembles for Sequence Learning

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This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement lear ...

École Polytechnique Fédérale de Lausanne2006

Discrmininant Models for Text-independent Speaker Verification

This thesis addresses text-independent speaker verification from a machine learning point of view. We use the machine learning framework to better define the problem and to develop new unbiased performance measures and statistical tests to compare objectiv ...

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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 ...

2005

Boosting word error rates

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We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...

2005

Boosting word error rates

Samy Bengio, Christos Dimitrakakis

2005

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

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 word error rates

Samy Bengio, Christos Dimitrakakis

IDIAP2004