Statistical learning in materials engineering |
Salvador NayaEscuela Politécnica Superior Grupo de investigación MODES Universidade da Coruña
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Javier Tarrío-SaavedraEscuela 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.
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Keywords: Nonlinear regression, Supervised classification, Image segmentation, Thermal analysis.
- AMS Subject classifications: 62P30, 62F99, 62H35.
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