ISBN: 9780323904087 - machine learning and pattern recognition methods in chemistry from multivariate and data driven modeling (9 resultados)

Condición: New.

Taschenbuch. Condición: Neu. Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling | Jahan B. Ghasemi | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2022 | Elsevier | EAN 9780323904087 | Verantwortliche Person für die EU: Elsevier B.V., Radarweg 29, 1043 NX AMSTERDAM, NIEDERLANDE, productsafety[at]elsevier[dot]com | Anbieter: preigu. …

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New. In English.

Condición: As New. Unread book in perfect condition.

Condición: New. Provides an introductory overview of statistical methods for the analysis and interpretation of chemical data Discusses the use of machine learning for recognizing patterns in multidimensional chemical data Identifies common sourc.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. Englisch.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…