Binding: Paperback
Description: 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.
Title: Machine Learning And Pattern Recognition Methods In Chemistry From Multivariate And Data Driven Modeling
Brand: Elsevier - Health Sciences Division
Barcode: 9780323904087
Pages: 216 Pages
Publication Date: 10/20/2022
Category: Pattern Recognition