Search results for "Variable selection"
showing 4 items of 24 documents
Analyses spectrale et texturale de données haute résolution pour la détection automatique des maladies de la vigne
2019
‘Flavescence dorée’ is a contagious and incurable disease present on the vine leaves. The DAMAV project (Automatic detection of Vine Diseases) aims to develop a solution for automated detection of vine diseases using a micro-drone. The goal is to offer a turnkey solution for wine growers. This tool will allow the search for potential foci, and then more generally any type of detectable vine disease on the foliage. To enable this diagnosis, the foliage is proposed to be studied using a dedicated high-resolution multispectral camera.The objective of this PhD-thesis in the context of DAMAV is to participate in the design and implementation of a Multi-Spectral (MS) image acquisition system and …
Estimation of sparse generalized linear models: the dglars package
2013
dglars is a public available R package that implements the method proposed in Augugliaro, Mineo and Wit (2013) developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method (LARS). The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve; specifically a predictor-corrector algorithm and a cyclic coordinate descent algorithm.
Model uncertainty and variable selection: an application to the modelization of FDI determinants in Europe
2019
Las últimas décadas han visto un interés cada vez mayor en la IED, y un debate creciente sobre su modelización en términos de las variables consideradas como sus determinantes, la especificación del modelo y los métodos de estimación del modelo de gravedad de la IED. Esto se debe a la incertidumbre que rodea tanto las teorías como los enfoques empíricos de la IED. Esta Tesis doctoral tiene como objetivo contribuir a la literatura mediante la investigación de las fuerzas impulsoras de las actividades de las EMNs hacia y desde los países europeos, tanto a nivel regional como nacional, abordando los problemas de selección de variables e incertidumbre del modelo que se enfrentan al modelizar la…
Intelligent solutions for real-life data-driven applications
2017
The subject of this thesis belongs to the topic of machine learning or, specifically, to the development of advanced methods for regression analysis, clustering, and anomaly detection. Industry is constantly seeking improved production practices and minimized production time and costs. In connection to this, several industrial case studies are presented in which mathematical models for predicting paper quality were proposed. The most important variables for the prediction models are selected based on information-theoretic measures and regression trees approach. The rest of the original papers are devoted to unsupervised machine learning. The main focus is developing advanced spectral cluster…