Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling - Softcover

Softcover

ISBN 10: 0323904084 ISBN 13: 9780323904087

Publisher: Elsevier, 2022

Synopsis

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.

• 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 sources of multivariate errors

About this title

About the Author

Dr. Jahan B Ghasemi received his PhD from Shiraz University. He was a visiting Scientist at the University of Chalmers in 2001 and Delaware University in 2006. His current research interests are focused on chemometrics and data analysis and computational drug design. He is the author of more than 200 papers and 4 chapter books in international journals and books.

From the Back Cover

Multivariate chemical data can provide researchers with valuable insights, but accurately using tools such as machine learning or pattern recognition to analyze such data requires a clear understanding of the abilities and limitations of these techniques. Multivariate Data-driven Modeling, Machine Learning and Pattern Recognition Methods in Chemistry outlines key knowledge in this area, combining critical introductory approaches with the latest advance techniques.

Beginning with an introduction of univariate and multivariate statistical analysis, the book goes on to explore multivariate calibration and validation methods in more detail. Soft modeling in chemical data analysis, hyperspectral data analysis, and autoencoder applications in analytical chemistry are then discusses in more detail, providing useful examples of the techniques discussed for chemistry applications.

Drawing on the knowledge of a global team of researchers, Multivariate Data-driven Modeling, Machine Learning and Pattern Recognition Methods in Chemistry is a helpful guide for any chemists interested in developing their skills in multivariate data and error analysis.

Edition details

• Publisher: Elsevier
• Publication date: 2022
• Language: English
• ISBN 10: 0323904084
• ISBN 13: 9780323904087
• Binding: Paperback
• Edition number: 1
• Number of pages: 210
• Editor: Ghasemi