@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

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              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.
            """ ;
    dct:identifier "NNT: 2012DENS0062" ;
    dct:issued "2026-05-08T04:52:58.427067"^^xsd:dateTime ;
    dct:language "en" ;
    dct:modified "2026-05-08T04:52:58.427071"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Learning algorithms and statistical software, with applications to bioinformatics" ;
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        "mathmath-gmmathematics-mathgeneral-mathematics-mathgm",
        "optimisation-convexe",
        "sdvsalife-sciences-q-bioagricultural-sciences",
        "theses" ;
    dcat:landingPage <https://theses.hal.science/tel-00906029> .

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    dct:title "Learning algorithms and statistical software, with applications to bioinformatics" ;
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