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Towards a supervised classification of neocortical interneuron morphologies

Publications associées (33)

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Intrusion Detection in MANET Using Classification Algorithms: The Effects of Cost and Model Selection

Christos Dimitrakakis, Aikaterini Mitrokotsa

Intrusion detection is frequently used as a second line of defense in Mobile Ad-hoc Networks (MANETs). In this paper we examine how to properly use classification methods in intrusion detection for MANETs. In order to do so we evaluate five supervised clas ...

Elsevier2013

Sparse Binary Features for Image Classification

Johann Paratte

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes imp ...

2013

Inverse Solutions for Brain Computer Interface

José del Rocio Millán Ruiz, Mohit Kumar Goel

Inverse solution allows to estimate sources that generate a given scalp EEG topography. Recently, it has been used in Brain Computer Interfaces (BCIs) to extract robust features based on the hypothesis that projection onto the source space (high dimensiona ...

EPFL2013

Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

Jean-Philippe Thiran, Volker Gass, Frank Grégoire Jean de Morsier, Maurice Borgeaud

Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise ...

Ieee2012

Discovering single classes in remote sensing images with active learning

Devis Tuia

When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. More ...

2012

Learning from Candidate Labeling Sets

Jie Luo

In many real world applications we do not have access to fully-labeled training data, but only to a list of possible labels. This is the case, e.g., when learning visual classifiers from images downloaded from the web, using just their text captions or tag ...

Idiap2011

Learning from Candidate Labeling Sets

Jie Luo

MIT Press2010

Semantic coding by supervised dimensionality reduction

Pascal Frossard, Effrosyni Kokiopoulou

This paper addresses the problem of representing multimedia information under a compressed form that permits efficient classification. The semantic coding problem starts from a subspace method where dimensionality reduction is formulated as a matrix factor ...

2008

SimpleMKL

Multiple kernel learning (MKL) aims at simultaneously learning a kernel and the associated predictor in supervised learning settings. For the support vector machine, an efficient and general multiple kernel learning algorithm, based on semi-infinite linear ...

2008

Feature Extraction for Multi-Class BCI using Canonical Variates Analysis

José del Rocio Millán Ruiz, Pierre Ferrez

Objective: To propose a new feature extraction method with canonical solution for multi-class Brain-Computer Interfaces (BCI). The proposed method should provide a reduced number of canonical discriminant spatial patterns (CDSP) and rank the channels sorte ...

2007