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Statistical learning in materials engineering

Salvador Naya
Escuela Politécnica Superior
Grupo de investigación MODES
Universidade da Coruña
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Javier Tarrío-Saavedra
Escuela Politécnica Superior
Grupo de investigación MODES
Universidade da Coruña
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  • Abstract
    In this work we present different applications of statistical techniques such as modeling or supervised classification of engineering materials. Many of these techniques stack could be included within the Statistical Learning. Statistical Learning refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science. With the explosion of “Big Data” problems, statistical learning has become a very hot field in many scientific areas as well as material engineering. The classification studies, analysis of variance and estimation of important materials characteristics are nowdays crucial in engineering. The proposed statistical learning algorithms have been performed using the R statistical software.
  • Keywords: Nonlinear regression, Supervised classification, Image segmentation, Thermal analysis.
  • AMS Subject classifications: 62P30, 62F99, 62H35.
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