Search results for "Machine learning"

Reports and other PDF Documents

2014

Stata users often need to combine text, tables, and figures. The author's command, lpdf, generates reports and other PDF documents. lpdf compiles text stored in global macros, tables stored as dataset tables or LATEX table input files, and figures stored as Stata graphs or PDF figure files. LATEX must be installed, but familiarity with LATEX is not necessary. lpdf performs every step through Stata and with Stata syntax. It generates documents in report or article style and portrait or landscape orientation. The default author name, document title, and date can be modified. Further format options include the font and margin sizes. For each table and figure, the width and layout can be adapt…

Adaptive Learning Process for the Evolution of Ontology-Described Classification Model in Big Data Context

2016

International audience; One of the biggest challenges in Big Data is to exploit value from large volumes of variable and changing data. For this, one must focus on analyzing the data in these Big Data sources and classify the data items according to a domain model (e.g. an ontology). To automatically classify unstructured text documents according to an ontology, a hierarchical multi-label classification process called Semantic HMC was proposed. This process uses ontologies to describe the classification model. To prevent cold start and user overload, the classification process automatically learns the ontology-described classification model from a very large set of unstructured text documen…

Overlapping community detection versus ground-truth in AMAZON co-purchasing network

2015

International audience; Objective evaluation of community detection algorithms is a strategic issue. Indeed, we need to verify that the communities identified are actually the good ones. Moreover, it is necessary to compare results between two distinct algorithms to determine which is most effective. Classically, validations rely on clustering comparison measures or on quality metrics. Although, various traditional performance measures are used extensively. It appears very clearly that they cannot distinguish community structures with different topological properties. It is therefore necessary to propose an alternative methodology more sensitive to the community structure variations in orde…

Une approche Web sémantique et combinatoire pour un système de recommandation sensible au contexte appliqué à l'apprentissage mobile

2014

National audience; Au vu de l'émergence rapide des nouvelles technologies mobiles et la croissance des offres et besoins d'une société en mouvement en formation, les travaux se multiplient pour identifier de nouvelles plateformes d'apprentissage pertinentes afin d'améliorer et faciliter le processus d'apprentissage à distance. La prochaine étape de l'apprentissage à distance est naturellement le port de l'e-learning (apprentissage électronique) vers les nouveaux systèmes mobiles. On parle alors de m-learning (apprentissage mobile). La recherche d'informations dans le domaine du m-learning peut être définie comme une activité dont la fi-nalité est de localiser et de délivrer des contenus d'a…

Construction de Modèles Prédictifs pour l'Analyse des Relations Oiseaux-Paysage

2013

National audience; Cet article présente une comparaison de trois méthodes (Modèles Linéaires Généralisés, Réseaux de Neurones, Machines Vecteurs Supports) et de différentes combinaisons de prétraitements de données (filtrage, arrondi, analyse factorielle, sélection de paramètres). L'objectif de cette comparaison est de définir quel est le processus qui permet de construire le meilleur modèle prédictif, dans le cadre de la prédiction d'abondances d'espèces d'oiseaux à partir de variables décrivant le paysage. Nous comparerons les modèles grâce à l'erreur moyenne absolue et à l'information mutuelle. Cette comparaison a montré qu'aucune technique étudiée ne permet de construire des modèles pré…

Numerical models contribute to expand the sweet taste chemical space

2021

A Neural Network Meta-Model and its Application for Manufacturing

2015

International audience; Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique, called neural networks (NNs), to extract meaningful information from large data sets, so called big data. However, the application of NN to manufacturing problems remains limited because it involves the specialized skills of a data scientist. This paper introduces an appr…

Système de sécurité biométrique multimodal par imagerie, dédié au contrôle d’accès

2019

Research of this thesis consists in setting up efficient and light solutions to answer the problems of securing sensitive products. Motivated by a collaboration with various stakeholders within the Nuc-Track project, the development of a biometric security system, possibly multimodal, will lead to a study on various biometric features such as the face, fingerprints and the vascular network. This thesis will focus on an algorithm and architecture matching, with the aim of minimizing the storage size of the learning models while guaranteeing optimal performances. This will allow it to be stored on a personal support, thus respecting privacy standards.

Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series

2020

L'analyse prédictive permet d'estimer les tendances des évènements futurs. De nos jours, les algorithmes Deep Learning permettent de faire de bonnes prédictions. Cependant, pour chaque type de problème donné, il est nécessaire de choisir l'architecture optimale. Dans cet article, les modèles Stack-LSTM, CNN-LSTM et ConvLSTM sont appliqués à une série temporelle d'images radar sentinel-1, le but étant de prédire la prochaine occurrence dans une séquence. Les résultats expérimentaux évalués à l'aide des indicateurs de performance tels que le RMSE et le MAE, le temps de traitement et l'index de similarité SSIM, montrent que chacune des trois architectures peut produire de bons résultats en fon…

hidden markov random fields and cuckoo search method for medical image segmentation

2020

Segmentation of medical images is an essential part in the process of diagnostics. Physicians require an automatic, robust and valid results. Hidden Markov Random Fields (HMRF) provide powerful model. This latter models the segmentation problem as the minimization of an energy function. Cuckoo search (CS) algorithm is one of the recent nature-inspired meta-heuristic algorithms. It has shown its efficiency in many engineering optimization problems. In this paper, we use three cuckoo search algorithm to achieve medical image segmentation.