£131.39

Elsevier Machine Learning and Pattern Recognition in Chemistry

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Description

Expand your expertise in chemical data analysis with Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling. Published by Elsevier, this book provides a comprehensive guide for those looking to master both fundamental and advanced modeling techniques. The text begins with a clear introduction to univariate and multivariate statistical analysis, building a strong foundation before moving into complex multivariate calibration and validation methods. It is designed to bridge the gap between basic statistical approaches and modern computational techniques used in contemporary research. Readers will explore specialized topics including soft modeling in chemical data analysis, hyperspectral data analysis, and the application of autoencoders in analytical chemistry. By providing practical examples of these techniques within the field of chemistry, this resource serves as a valuable tool for understanding how data-driven modeling can be applied to solve complex chemical problems.

Key Features

Covers fundamental univariate and multivariate statistical analysis to build a solid knowledge base for chemical modeling.

Explores advanced multivariate calibration and validation methods for precise chemical data interpretation.

Provides detailed insights into soft modeling techniques specifically for chemical data analysis applications.

Includes specialized sections on hyperspectral data analysis to support modern spectroscopic research.

Discusses autoencoder applications in analytical chemistry to introduce cutting-edge machine learning tools.

Uses practical examples to demonstrate how various modeling techniques are applied directly to chemistry.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
20 October 2022
Listed Since
10 November 2021

Barcode

No barcode data available

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