Concept

Grand canonical ensemble

Related publications (119)

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

2004

On duality of canonical representations

Stefano Picozzi

Every convex polyhedron in

R^d

admits both H- and V-representations. In both cases, a representation is defined to be canonical if it is minimal and unique up to simple transformations. In general, canonical H- and V-representations are discussed separat ...

2004

Dissipative evolution of quantum statistical ensembles and nonlinear response to a time-periodic perturbation

Jean-Philippe Ansermet, François Reuse, Jacques Van Der Klink

We present a detailed discussion of the evolution of a statistical ensemble of quantum mechanical systems coupled weakly to a bath. The Hilbert space of the full system is given by the tensor product between the Hilbert spaces associated with the bath and ...

2003

Variance Reduction Techniques in Biometric Authentication

Samy Bengio, Norman Hoon Thian Poh

In this paper, several approaches that can be used to improve biometric authentication applications are proposed. The idea is inspired by the ensemble approach, i.e., the use of several classifiers to solve a problem. Compared to using only one classifier, ...

IDIAP2003

Brain states analysis for direct brain-computer communication

Touradj Ebrahimi, Julien Freudiger

In order to permit a brain computer efficient communication, it is important to dispose of an efficient algorithm to decode the brain electrical activity. We will focus our attention on an algorithm based on microstates segmentation of the brain electrical ...

2003

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

IDIAP2003

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

IDIAP2003

Network allometry

Andrea Rinaldo, Amos Maritan

[1] We derive a new allometric scaling law for loopless networks, which we confirm with studies on rivers, exact network results and computer simulations. We provide evidence suggesting that ensemble averaging of the allometric property (where individual r ...

2002

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On duality of canonical representations

Stefano Picozzi

Every convex polyhedron in

R^d

2004