Publications related to Ensemble learning

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, 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

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

Real-Time Face Detection Using Boosting Learning in Hierarchical Feature Spaces

Daniel Gatica-Perez, Dong Zhang

Boosting-based methods have recently led to the state-of-the-art face detection systems. In these systems, weak classifiers to be boosted are based on simple, local, Haar-like features. However, it can be empirically observed that in later stages of the bo ...

IDIAP2003

New Entropy Based Combination Rules in HMM/ANN Multi-stream ASR

Hervé Bourlard, Hemant Misra, Vivek Tyagi

Classifier performance is often enhanced through combining multiple streams of information. In the context of multi-stream HMM/ANN systems in ASR, a confidence measure widely used in classifier combination is the entropy of the posteriors distribution outp ...

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

Non-Linear 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

Non-Linear 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, ...

2003

Boosting Pixel-based Classifiers for Face Verification

Sébastien Marcel, Yann Rodriguez

The performance of face verification systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machine ...

IDIAP2003

Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS

Sébastien Marcel

We compare two classifier approaches, namely classifiers based on Multi Layer Perceptrons (MLPs) and Gaussian Mixture Models (GMMs), for use in a face verification system. The comparison is carried out in terms of performance, robustness and practicability ...

2003

Ensemble learning

Online Policy Adaptation for Ensemble Classifiers

Online Policy Adaptation for Ensemble Classifiers

Boosting HMMs with an application to speech recognition

Real-Time Face Detection Using Boosting Learning in Hierarchical Feature Spaces

New Entropy Based Combination Rules in HMM/ANN Multi-stream ASR

Variance Reduction Techniques in Biometric Authentication

Non-Linear Variance Reduction Techniques in Biometric Authentication

Non-Linear Variance Reduction Techniques in Biometric Authentication

Boosting Pixel-based Classifiers for Face Verification

Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS

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New Entropy Based Combination Rules in HMM/ANN Multi-stream ASR

Boosting HMMs with an application to speech recognition

Online Policy Adaptation for Ensemble Classifiers

Non-Linear Variance Reduction Techniques in Biometric Authentication

Variance Reduction Techniques in Biometric Authentication

Real-Time Face Detection Using Boosting Learning in Hierarchical Feature Spaces

Online Policy Adaptation for Ensemble Classifiers

Boosting Pixel-based Classifiers for Face Verification

Non-Linear Variance Reduction Techniques in Biometric Authentication

Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS