Concept

Ensemble learning

Related publications (493)

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Probabilistic models for multi-classifier biometric authentication using quality measures

Jonas Richiardi

Biometric authentication can be cast as a signal processing and statistical pattern recognition problem. As such, it relies on models of signal representations that can be used to discriminate between classes. One of the assumptions typically made by the p ...

EPFL2008

Keypoint Signatures for Fast Learning and Recognition

Pascal Fua, Vincent Lepetit, Michael Calonder

Statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the training itself is usually relatively slow and performed offline. Although methods hav ...

2008

Ensembles of SVMs using an Information Theoretic Criterion

Jean-Philippe Thiran, Julien Meynet

Training Support Vector Machine can become very challenging in large scale problems. Training several lower complexity SVMs on local subsets of the training set can significantly reduce the training complexity and also improve the classification performanc ...

2008

Multi-layer Boosting for Pattern Recognition

François Fleuret

We extend the standard boosting procedure to train a two-layer classifier dedicated to handwritten character recognition. The scheme we propose relies on a hidden layer which extracts feature vectors on a fixed number of points of interest, and an output l ...

Idiap2008

Keypoint Signatures for Fast Learning and Recognition

Pascal Fua, Vincent Lepetit, Michael Calonder

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

On the Combination of Auditory and Modulation Frequency Channels for ASR applications

Hynek Hermansky, Fabio Valente

This paper investigates the combination of evidence coming from different frequency channels obtained filtering the speech signal at different auditory and modulation frequencies. In our previous work \cite{icassp2008}, we showed that combination of classi ...

IDIAP2008

Bayesian Feature Selection Applied In a P300 Brain- Computer Interface

Touradj Ebrahimi, Jean-Marc Vesin, Ashkan Yazdani, Ulrich Hoffmann

Feature selection is a machine learning technique that has many interesting applications in the area of brain- computer interfaces (BCIs). Here we show how automatic relevance determination (ARD), which is a Bayesian feature selection technique, can be app ...

2008

Decision Tree Learning for Drools

Gizil Oguz

Decision trees can be used to represent a large number of expert system rules in a compact way. We describe machine learning algorithms for learning decision trees. We have implemented the algorithms, including bagging and boosting techniques. We have depl ...

2008

A Discriminative Kernel-based Model to Rank Images from Text Queries

Samy Bengio, David Grangier

This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance. The ...

2008

Machine learning for information retrieval

David Grangier

In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...

EPFL2008