Publications associées à Binary classification

Decoding brain states from fMRI connectivity graphs

Dimitri Nestor Alice Van De Ville, Jonas Richiardi

Functional connectivity analysis of fMRI data can reveal synchronised activity between anatomically distinct brain regions. Here, we extract the characteristic connectivity signatures of different brain states to perform classification, allowing us to deco ...

2011

Boosting Localized Features for Speaker and Speech Recognition

Anindya Roy

In this thesis, we propose a novel approach for speaker and speech recognition involving localized, binary, data-driven features. The proposed approach is largely inspired by similar localized approaches in the computer vision domain. The success of these ...

Ecole Polytechnique Federale de Lausanne (EPFL)2011

Detecting Anomalies to Improve Classification Performance in Opportunistic Sensor Networks

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hesam Sagha

Anomalies and changes in sensor networks which are deployed for activity recognition may abate the classification performance. Detection of anomalies followed by compensatory reaction would ameliorate the performance. This paper introduces a novel approach ...

2011

Learning parameterized histogram kernels on the simplex manifold for image and action classification

Vitaly Henry Ablavsky

State-of-the-art image and action classification systems often employ vocabulary-based representations. The classification accuracy achieved with such vocabulary-based representations depends significantly on the chosen histogram-distance. In particular, w ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Rgtsp: a generalized top scoring pairs package for class prediction

Mauro Delorenzi, Vlad Popovici

A top scoring pair (TSP) classifier consists of a pair of variables whose relative ordering can be used for accurately predicting the class label of a sample. This classification rule has the advantage of being easily interpretable and more robust against ...

Oxford University Press2011

Multispectral SIFT for Scene Category Recognition

Sabine Süsstrunk, Matthew Brown

We use a simple modification to a conventional SLR camera to capture images of several hundred scenes in colour (RGB) and near-infrared (NIR). We show that the addition of near-infrared information leads to significantly improved performance in a scene-rec ...

IEEE Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

EEG Single-Trial Classification of Visual, Auditive and Vibratory Feedback Potentials in Brain-Computer Interfaces

Inaki Asier Iturrate Gil, Marco Creatura

Feedback stimuli are fundamental components in Brain-Computer Interfaces. It is known that the presentation of feedback stimuli elicits certain brain potentials that can be measured and classified. As stimuli can be given through different sensory modaliti ...

2011

Cognitive and social effects of handwritten annotations

Pierre Dillenbourg, Frédéric Kaplan, Andrea Mazzei

This article first describes a method for extracting and classifying handwritten annotations on printed documents using a simple camera integrated in a lamp. The ambition of such a research is to offer a seamless integration of notes taken on printed paper ...

2011

Spatio-temporal filtering for EEG error related potentials.

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil

This paper presents a new filter for EEG Event-Related Potentials that relies on spatio-temporal features. The results are analyzed with error-related potentials, and compared to the results obtained with Independent Component Analysis. Additionally, the o ...

2011

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