Publications related to Ensemble creation and reconfiguration for activity recognition: An information theoretic approach.

Boosting HMMs with an application to speech recognition

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

Kernel-based classifiers with applications to face detection

Vlad Popovici

Statistical pattern recognition occupies a central place in the general context of machine learning techniques, as it provides the theoretical insights and the practical means for solving a variety of problems ranging from character recognition to face rec ...

EPFL2004

New support vector algorithms for multicategorical data applied to real-time object recognition

Silvio Borer

Humans have the ability to learn. Having seen an object we can recognise it later. We can do this because our nervous system uses an efficient and robust visual processing and capabilities to learn from sensory input. On the other hand, designing algorithm ...

EPFL2003

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

Why Max-min Fairness Is Not Suitable For Multi-Hop Wireless Networks

Jean-Yves Le Boudec

We consider the issue of which criteria to use when evaluating the design of a wireless multihop network. It is known, and we illustrate in this paper, that maximizing the total capacity, or transport capacity, leads to gross imbalance and is not suitable. ...

2003

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

Ensemble creation and reconfiguration for activity recognition: An information theoretic approach.

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Boosting HMMs with an application to speech recognition

Boosting HMMs with an application to speech recognition

Online Policy Adaptation for Ensemble Classifiers

Kernel-based classifiers with applications to face detection

New support vector algorithms for multicategorical data applied to real-time object recognition

Boosting HMMs with an application to speech recognition

Online Policy Adaptation for Ensemble Classifiers

Online Policy Adaptation for Ensemble Classifiers

Why Max-min Fairness Is Not Suitable For Multi-Hop Wireless Networks

Online Policy Adaptation for Ensemble Algorithms

Boosting HMMs with an application to speech recognition

Kernel-based classifiers with applications to face detection

New support vector algorithms for multicategorical data applied to real-time object recognition

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

Online Policy Adaptation for Ensemble Classifiers

Why Max-min Fairness Is Not Suitable For Multi-Hop Wireless Networks

Online Policy Adaptation for Ensemble Algorithms