Publications by Christos Dimitrakakis

Gradient estimates of return distributions

We present a general method for maintaining estimates of the distribution of parameters in arbitrary models. This is then applied to the estimation of probability distributions over actions in value-based reinforcement learning. While this approach is simi ...

2005

Boosting word error rates

Samy Bengio, Christos Dimitrakakis

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 ...

IDIAP2004

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

Estimates of Parameter Distributions for Optimal Action Selection

Samy Bengio, Christos Dimitrakakis

We present a general method for maintaining estimates of the distribution of parameters in arbitrary models. This is then applied to the estimation of probability distribution over actions in value-based reinforcement learning. While this approach is simil ...

IDIAP2004

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

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

Christos Dimitrakakis

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Gradient estimates of return distributions

Boosting word error rates

Boosting HMMs with an application to speech recognition

Boosting HMMs with an application to speech recognition

Online Policy Adaptation for Ensemble Classifiers

Estimates of Parameter Distributions for Optimal Action Selection

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 Algorithms

Estimates of Parameter Distributions for Optimal Action Selection

Gradient estimates of return distributions

Boosting word error rates

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

Boosting HMMs with an application to speech recognition

Online Policy Adaptation for Ensemble Classifiers

Online Policy Adaptation for Ensemble Algorithms