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Learning algorithms and statistical software, with applications to bioinformatics

Toby Dylan Hocking 1, 2 
2 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique - ENS Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : 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.
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Submitted on : Tuesday, November 19, 2013 - 10:27:08 AM
Last modification on : Thursday, March 17, 2022 - 10:08:43 AM
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  • HAL Id : tel-00906029, version 1



Toby Dylan Hocking. Learning algorithms and statistical software, with applications to bioinformatics. General Mathematics [math.GM]. École normale supérieure de Cachan - ENS Cachan, 2012. English. ⟨NNT : 2012DENS0062⟩. ⟨tel-00906029⟩



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