Communication sciences (3) Geography (3) Economies and finances (3) Methods and statistics (2) Sociology (1)

Economies and finances (2) Methods and statistics (1) Communication sciences (1) Education (1) Law (1)

Articles (15) Conferences and symposiums (14) Preprint (6) Textual materials (1) Reviews (1) Learning Object (1) Books and book chapters (1)

science (24) Science (24) Science (24) science (24) Learning (22) Knowledge and learning (17) show (16) approach (15) (15) classification (14) Classification (14) classification (14) paper (13) Paper (13) method (12) Algorithms (12) Methods (10) Classification (9) (9) Support vector machines (7) Méthode (7) Signals and signaling (6) Example (6) Señales y señalización (6) Señales marítimas (6) Signaux et signalisation (6) support (6) regression (6) representation (5) Speeches, addresses, etc (5)

thesis (14) (13) Learning (12) Apprentissage (11) Méthodes (11) apprenticeship (11) analysis (9) Analysis (9) information (8) enforcement (7) machinery (7) assay (7) Assaying (7) Algorithms (7) Analyse (7) Analyse (7) number (7) science (6) classification (6) Communication (6) Science (6) Classification (6) Information (6) Classification (6) Information (6) science (6) classification (6) resolution (act) (5) model (5) Computer algorithms (5)

Alain Rakotomamonjy
et al. (Oct 15, 2020)

Preprint

Owing to their statistical properties, non-convex sparse regularizers have attracted much interest for estimating a sparse linear model from high dimensional data. Given that the solution is sparse, for accelerating convergence, a working set strategy addresses the optimization problem through an it...

Alain Rakotomamonjy
et al. (Oct 15, 2020)

Preprint

We address the problem of unsupervised domain adaptation under the setting of generalized target shift (both class-conditional and label shifts occur). We show that in that setting, for good generalization, it is necessary to learn with similar source and target label distributions and to match the...

Conferences and symposiums

Alain Rakotomamonjy
et al. (2019)

Conferences and symposiums

Leveraging on the convexity of the Lasso problem , screening rules help in accelerating solvers by discarding irrelevant variables, during the optimization process. However, because they provide better theoretical guarantees in identifying relevant variables, several non-convex regulariz-ers for the...

Preprint

Alain Rakotomamonjy
et al. (Nov 7, 2018)

Preprint

This paper presents a dissimilarity-based discriminative framework for learning from data coming in the form of probability distributions. Departing from the use of positive kernel-based methods, we build upon embeddings based on dissimilarities tailored for distribution. We enable this by extending...

Conferences and symposiums

Rafael Will M. de Araujo
et al. (Apr 15, 2018)

Conferences and symposiums

International audience

Conferences and symposiums

Rafael Will M De Araujo
et al. (2018)

Conferences and symposiums

Traditional dictionary learning methods are based on quadratic convex loss function and thus are sensitive to outliers. In this paper, we propose a generic framework for robust dictionary learning based on concave losses. We provide results on composition of concave functions, notably regarding supe...

Books and book chapters

Rémi Flamary
et al. (Jul 29, 2016)

Books and book chapters

International audience

Alain Rakotomamonjy
et al. (2016)

Articles

We introduce a novel algorithm for solving learning problems where both the loss function and the regularizer are non-convex but belong to the class of difference of convex (DC) functions. Our contribution is a new general purpose proximal Newton algorithm that is able to deal with such a situation....

Conferences and symposiums

Alain Rakotomamonjy
et al. (Aug 31, 2015)

Conferences and symposiums

International audience

Alain Rakotomamonjy
et al. (2015)

Articles

This paper addresses the problem of audio scenes classification and contributes to the state of the art by proposing a novel feature. We build this feature by considering histogram of gradients (HOG) of time-frequency representation of an audio scene. Contrarily to classical audio features like MFCC...

Alain Rakotomamonjy
et al. (2013)

Articles

We propose a principled framework for learning with infinitely many features, situations that are usually induced by continuously parametrized feature extraction methods. Such cases occur for instance when considering Gabor-based features in computer vision problems or when dealing with Fourier feat...

Nisrine Jrad
et al. (Aug 30, 2011)

Articles

In many machine learning applications, like Brain-Computer Interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and Signal to Noise Ratio (SNR) is not homogeneously spread across sensors. Thus, collected data are highly variable and disc...

Alain Rakotomamonjy
et al. (2011)

Articles

Recently, there has been a lot of interest around multi-task learning (MTL) problem with the constraints that tasks should share a common sparsity profile. Such a problem can be addressed through a regularization framework where the regularizer induces a joint-sparsity pattern between task decision...

Reviews

Stéphane Canu
et al. (2011)

Reviews

Kernel Machines is a term covering a large class of learning algorithms, including Splines and support vector machines (SVM) as a particular instance. Kernel Machines is an important and active field of all Machine Learning research. Not only the number of publications bear witness of this fact but...

Preprint

Florian Yger
et al. (Aug, 2010)

Preprint

This paper addresses the problem of optimal feature extraction from a wavelet representation. Our work aims at building features by selecting wavelet coefficients resulting from signal or image decomposition on a adapted wavelet basis. For this purpose, we jointly learn in a kernelized large-margin...

Rémi Flamary
et al. (Jul 6, 2010)

Textual materials

We address in this paper the problem of multi-channel signal sequence labeling. In particular, we consider the problem where the signals are contaminated by noise or may present some dephasing with respect to their labels. For that, we propose to jointly learn a SVM sample classifier with a temporal...

Gilles Gasso
et al. (2009)

Articles

This paper considers the problem of recovering a sparse signal representation according to a signal dictionary. This problem could be formalized as a penalized least-squares problem in which sparsity is usually induced by a l1 -norm penalty on the coefficients. Such an approach known as the Lasso or...

Gilles Gasso
et al. (Aug 1, 2008)

Preprint

This paper considers the problem of recovering a sparse signal representation according to a signal dictionary. This problem is usually formalized as a penalized least-squares problem in which sparsity is usually induced by a l1 -norm penalty on the coefficient. Such an approach known as the Lasso o...

Conferences and symposiums

Yves Grandvalet
et al. (2008)

Conferences and symposiums

We consider the problem of binary classification where the classifier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow's rule, is defined by two thresholds on posterior probabilities. From simple desiderata, namely the consistency and the spa...

Articles

Alain Rakotomamonjy
et al. (2008)

Articles

Multiple kernel learning 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 (MKL) algorithm, based on semi-infinite linear progamming, has been recently proposed. Thi...

Alain Rakotomamonjy
et al. (2008)

Articles

Brain-Computer Interface P300 speller aims at helping patients unable to activate muscles to spell words by means of their brain signal activities. Associated to this BCI paradigm, there is the problem of classifying electroencephalogram signals related to responses to some visual stimuli. This pape...

Alain Rakotomamonjy
(2007)

Articles

This paper addresses the problem of variable ranking for support vector regression. The ranking criteria that we proposed are based on leave-one-out bounds and some variants and for these criteria we have compared different search-space algorithms: recursive feature elimination and scaling factor op...

Francois CABESTAING
et al. (2007)

Articles

Les interfaces cerveau-machine (BMI: Brain-Machine Interface) sont des systèmes de communication directe entre un individu et une machine ne reposant pas sur les canaux de communication standard que sont nos nerfs périphériques et nos muscles. Dans une BMI, l'activité cérébrale de l'utilisateur est...

Riikka Huusari
(Nov 7, 2019)

Thesis

Nowadays datasets with non-standard structures are more and more common. Examples include the already well-known multi-task framework where each data sample is associated with multiple output labels, as well as the multi-view learning paradigm, in which each data sample can be seen to contain numero...

Neelanjan Bhowmik
(Nov 7, 2017)

Thesis

Content-Based Image Retrieval (CBIR) is a discipline of Computer Science which aims at automatically structuring image collections according to some visual criteria. The offered functionalities include the efficient access to images in a large database of images, or the identification of their conte...

Romain Huet
(Jun 19, 2017)

Thesis

The neural networks have gained a renewed interest through the deep learning paradigm. Whilethe so called optimised neural nets, by optimising the parameters necessary for learning, require massive computational resources, we focus here on neural nets designed as addressable content memories, or neu...

Caroline Chabault
(Jan 1, 2017)

Articles

Convention de procédure participative, Rupture conventionnelle… Quelle place pour le juge ? Le développement des modes alternatifs de règlement des différends est aujourd'hui tel-et ce notamment depuis la loi n°2016-1547 du 18 novembre 2016 de modernisation de la justice du XXIème siècle-que le juge...

A Rakotomamonjy
et al. (Aug 18, 2016)

Preprint

Several sparsity-constrained algorithms such as Orthogonal Matching Pursuit or the Frank-Wolfe algorithm with sparsity constraints work by iteratively selecting a novel atom to add to the current non-zero set of variables. This selection step is usually performed by computing the gradient and then b...

Abir Zribi
(Mar 17, 2016)

Thesis

This thesis arises in the context of computer aided analysis for subcellular protein localization in microscopic images. The aim is the establishment of an automatic classification system allowing to identify the cellular compartment in which a protein of interest exerts its biological activity. In...

Patric Nader
(Sep 24, 2015)

Thesis

The security of critical infrastructures has been an interesting topic recently with the increasing risk of cyber-attacks and terrorist threats against these systems. The majority of these infrastructures is controlled via SCADA (Supervisory Control And Data Acquisition) systems, which allow remote...

Néhémy Lim
(Apr 2, 2015)

Thesis

In multivariate time series analysis, existing models are often used for forecasting, i.e. estimating future values of the observed system based on previously observed values. Another purpose is to find causal relationships among a set of state variables within a dynamical system. We focus on the la...

Thi Bich Thuy Nguyen
(Dec 11, 2014)

Thesis

Image is one of the most important information in our lives. Along with the rapid development of digital image acquisition devices such as digital cameras, phone cameras, the medical imaging devices or the satellite imaging devices..., the needs of processing and analyzing images is more and more de...

Minh Thuy Ta
(Jul 4, 2014)

Thesis

This thesis focus on four problems in data mining and machine learning: clustering data streams, clustering massive data sets, weighted hard and fuzzy clustering and finally the clustering without a prior knowledge of the clusters number. Our methods are based on deterministic optimization approache...

Manh Cuong Nguyen
(May 19, 2014)

Thesis

Classification (supervised, unsupervised and semi-supervised) is one of important research topics of data mining which has many applications in various fields. In this thesis, we focus on developing optimization approaches for solving some classes of optimization problems in data classification. Fir...

Arnaud Fouchet
(Jan 10, 2014)

Thesis

New technologies in molecular biology, in particular dna microarrays, have greatly increased the quantity of available data. in this context, methods from mathematics and computer science have been actively developed to extract information from large datasets. in particular, the problem of gene regu...

Carolina Saavedra
(Nov 14, 2013)

Thesis

Brain-Computer Interfaces (BCI) are control and communication systems which were initially developed for people with disabilities. The idea behind BCI is to translate the brain activity into commands for a computer application or other devices, such as a spelling system. The most popular technique t...

Carolina Verónica Saavedra Ruiz
(Nov 14, 2013)

Thesis

Brain-Computer Interfaces (BCI) are control and communication systems which were initially developed for people with disabilities. The idea behind BCI is to translate the brain activity into commands for a computer application or other devices, such as a spelling system. The most popular technique t...

Others

Marco Congedo
(Oct 22, 2013)

Others

Electroencephalographic data recorded on the human scalp can be modeled as a linear mixture of underlying dipolar source generators. The characterization of such generators is the aim of several families of signal processing methods. In this HDR we consider in several details three of such families,...