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

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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.

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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.