Learning algorithms and statistical software, with applications to bioinformatics
type: Dataset
description: Statistical machine learning is a branch of mathematics concerned with developing algorithms for data analysis. This thesis presents new mathematical models and statistical software, and is organized into two parts. In the first part, I present several new algorithms for clustering and segmentation. Clustering and segmentation are a class of techniques that attempt to find structures in data. I discuss the following contributions, with a focus on applications to cancer data from bioinformatics. In the second part, I focus on statistical software contributions which are practical for use in everyday data analysis.
identifier: NNT: 2012DENS0062
issued: 2026-05-08T04:52:58.427067
language: en
modified: 2026-05-08T04:52:58.427071
contact point: Organization
  fn: CCSD
keyword: apprentissage
keyword: bioinformatics
keyword: bioinformatique
keyword: convex-optimization
keyword: infoeu-reposemanticsdoctoralthesis
keyword: learning-algorithms
keyword: mathmath-gmmathematics-mathgeneral-mathematics-mathgm
keyword: optimisation-convexe
keyword: sdvsalife-sciences-q-bioagricultural-sciences
keyword: theses
landing page: https://theses.hal.science/tel-00906029

Learning algorithms and statistical software, with applications to bioinformatics
type: Distribution
format: HTML
issued: 2026-05-08T04:52:58.441892
modified: 2026-05-08T04:52:58.414180
access URL: https://theses.hal.science/tel-00906029

test_moissonnage_selune
type: Agent