Chemical Pattern Recognition
Author: Oldřich Štrouf
Publisher: Wiley-Blackwell
Published: 1986
Total Pages: 234
ISBN-13:
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Author: Oldřich Štrouf
Publisher: Wiley-Blackwell
Published: 1986
Total Pages: 234
ISBN-13:
DOWNLOAD EBOOKAuthor: Kurt Varmuza
Publisher: Springer
Published: 1980
Total Pages: 0
ISBN-13: 9780387102733
DOWNLOAD EBOOKAuthor: Jahan B. Ghasemi
Publisher: Elsevier
Published: 2022-10-20
Total Pages: 212
ISBN-13: 0323907067
DOWNLOAD EBOOKMachine 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
Author: Peter C. Jurs
Publisher: Wiley-Interscience
Published: 1975
Total Pages: 200
ISBN-13:
DOWNLOAD EBOOKAuthor: Richard G. Brereton
Publisher: John Wiley & Sons
Published: 2009-09-28
Total Pages: 532
ISBN-13: 0470987251
DOWNLOAD EBOOKOver the past decade, pattern recognition has been one of the fastest growth points in chemometrics. This has been catalysed by the increase in capabilities of automated instruments such as LCMS, GCMS, and NMR, to name a few, to obtain large quantities of data, and, in parallel, the significant growth in applications especially in biomedical analytical chemical measurements of extracts from humans and animals, together with the increased capabilities of desktop computing. The interpretation of such multivariate datasets has required the application and development of new chemometric techniques such as pattern recognition, the focus of this work. Included within the text are: ‘Real world’ pattern recognition case studies from a wide variety of sources including biology, medicine, materials, pharmaceuticals, food, forensics and environmental science; Discussions of methods, many of which are also common in biology, biological analytical chemistry and machine learning; Common tools such as Partial Least Squares and Principal Components Analysis, as well as those that are rarely used in chemometrics such as Self Organising Maps and Support Vector Machines; Representation in full colour; Validation of models and hypothesis testing, and the underlying motivation of the methods, including how to avoid some common pitfalls. Relevant to active chemometricians and analytical scientists in industry, academia and government establishments as well as those involved in applying statistics and computational pattern recognition.
Author: D. Coomans
Publisher: Research Studies Press Limited
Published: 1986
Total Pages: 282
ISBN-13:
DOWNLOAD EBOOKAuthor:
Publisher:
Published: 1976
Total Pages: 136
ISBN-13:
DOWNLOAD EBOOKAuthor: Anthony J. Myles
Publisher:
Published: 2005
Total Pages: 272
ISBN-13:
DOWNLOAD EBOOKAuthor: Richard G. Brereton
Publisher: John Wiley & Sons
Published: 2009-06-29
Total Pages: 522
ISBN-13: 9780470746479
DOWNLOAD EBOOKOver the past decade, pattern recognition has been one of the fastest growth points in chemometrics. This has been catalysed by the increase in capabilities of automated instruments such as LCMS, GCMS, and NMR, to name a few, to obtain large quantities of data, and, in parallel, the significant growth in applications especially in biomedical analytical chemical measurements of extracts from humans and animals, together with the increased capabilities of desktop computing. The interpretation of such multivariate datasets has required the application and development of new chemometric techniques such as pattern recognition, the focus of this work. Included within the text are: ‘Real world’ pattern recognition case studies from a wide variety of sources including biology, medicine, materials, pharmaceuticals, food, forensics and environmental science; Discussions of methods, many of which are also common in biology, biological analytical chemistry and machine learning; Common tools such as Partial Least Squares and Principal Components Analysis, as well as those that are rarely used in chemometrics such as Self Organising Maps and Support Vector Machines; Representation in full colour; Validation of models and hypothesis testing, and the underlying motivation of the methods, including how to avoid some common pitfalls. Relevant to active chemometricians and analytical scientists in industry, academia and government establishments as well as those involved in applying statistics and computational pattern recognition.
Author: Peter Wing-On Kwan
Publisher:
Published: 1979
Total Pages: 240
ISBN-13:
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