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Apprentissage Statistique Supervisé

Abstract : This article deals with supervised statistical machine-learning as a tool for engineers. First, the main theoretical and methodological principles are briefly presented and explained. Then, the article presents the most common models and algorithms used for supervised learning: the main “classical” techniques (Multi-Layer Perceptron, Support Vector Machines, Decision Trees and Random Forests, Boosting) are explained, as well as deep-learning with Convolutional Neural Networks.
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Contributor : Fabien Moutarde Connect in order to contact the contributor
Submitted on : Tuesday, March 19, 2019 - 5:31:42 PM
Last modification on : Wednesday, November 17, 2021 - 12:31:06 PM


  • HAL Id : hal-02073288, version 1


Fabien Moutarde. Apprentissage Statistique Supervisé. Techniques de l'Ingenieur, Techniques de l'ingénieur, 2019. ⟨hal-02073288⟩



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