Concept

Binary classification

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Sparse Probabilistic Classifiers

The scores returned by support vector machines are often used as a confidence measures in the classification of new examples. However, there is no theoretical argument sustaining this practice. Thus, when classification uncertainty has to be assessed, it i ...

IDIAP2007

Information theoretic combination of classifiers with application to face detection

Julien Meynet

Combining several classifiers has become a very active subdiscipline in the field of pattern recognition. For years, pattern recognition community has focused on seeking optimal learning algorithms able to produce very accurate classifiers. However, empiri ...

EPFL2007

Endo-permutation modules, a guided tour

Jacques Thévenaz

Shortly after the final classification of all endo-trivial modules for a finite p- group P by Jon Carlson and Jacques Thévenaz, the complete classification of all endo-permutation modules was obtained by Serge Bouc (The Dade group of a p-group, Inventiones ...

EPFL Press2007

Person Identification by Using AR Model for EEG Signals

Gelareh Mohammadi

A direct connection between ElectroEncephaloGram (EEG) and the genetic information of individuals has been investigated by neurophysiologists and psychiatrists since 1960’s; and it opens a new research area in the science. This paper focuses on the person ...

2006

Detecting Intentional Mental Transitions in an Asynchronous BCI

José del Rocio Millán Ruiz, Pierre Ferrez

The inclusion of mental tasks transitions detection (MTTD) has proven a useful tool in guiding the transduction process of a BCI working under an asynchronous protocol. MTTD allows for the extraction of the signal's contextual information in order to infer ...

IDIAP2006

Efficient Nonparametric Bayesian Modelling with Sparse Gaussian Process Approximations

Matthias Seeger

Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of powerful kernel-based models to large datasets. We present a general framework based on t ...

2006

A Novel Replica Detection System using Binary Classifiers, R-trees, and PCA

Touradj Ebrahimi

Replica detection is a prerequisite for the discovery of copyright infringement and detection of illicit content. For this purpose, contentbased systems can be an efficient alternative to watermarking. Rather than imperceptibly embedding a signal, content- ...

IEEE2006

Constructing visual models with a latent space approach

Daniel Gatica-Perez, Jean-Marc Odobez, Florent Monay Michaud, Pedro Manuel Da Silva Quelhas

We propose the use of latent space models applied to local invariant features for object classification. We investigate whether using latent space models enables to learn patterns of visual co-occurrence and if the learned visual models improve performance ...

2006

Melanoma Recognition using Kernel Classifiers

Barbara Caputo, Tatiana Tommasi

Melanoma is the most deadly skin cancer. Early diagnosis is a current challenge for clinicians. Current algorithms for skin lesions classification focus mostly on segmentation and feature extraction. This paper instead puts the emphasis on the learning pro ...

IDIAP2006

Distributed SVM applied to image classification

Pascal Frossard, Effrosyni Kokiopoulou

This paper proposes an algorithm for distributed classification, based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a redundant dictionary of at ...

2006