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Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling - Jahan B. Ghasemi - Bog

Bag om Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling

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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  • Sprog:
  • Engelsk
  • ISBN:
  • 9780323904087
  • Indbinding:
  • Paperback
  • Sideantal:
  • 216
  • Udgivet:
  • 20. oktober 2022
  • Størrelse:
  • 151x19x227 mm.
  • Vægt:
  • 362 g.
  • 2-3 uger.
  • 12. december 2024
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  • BLACK WEEK

Medlemspris

Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling

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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