Machine Learning and Pattern Recognition Methods in Chemistry from
Multivariate and Data Driven Modeling outlines key knowledge in
this area, combining critical introductory approaches with the
latest advanced techniques. Beginning with an introduction of
univariate and multivariate statistical analysis, the book then
explores multivariate calibration and validation methods. Soft
modeling in chemical data analysis, hyperspectral data analysis,
and autoencoder applications in analytical chemistry are then
discussed, providing useful examples of the techniques in chemistry
applications. Drawing on the knowledge of a global team of
researchers, this book will be a helpful guide for chemists
interested in developing their skills in multivariate data and
error analysis.
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