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

Author: Jahan B. Ghasemi

Publisher: Elsevier

Published: 2022-10-20

Total Pages: 212

ISBN-13: 0323907067

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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

Chemometrics for Pattern Recognition
Chemometrics for Pattern Recognition

Author: Richard G. Brereton

Publisher: John Wiley & Sons

Published: 2009-09-28

Total Pages: 532

ISBN-13: 0470987251

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Over 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.

Pattern Recognition Approach to Data Interpretation
Pattern Recognition Approach to Data Interpretation

Author: Diane Wolff

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 226

ISBN-13: 146159331X

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An attempt is made in this book to give scientists a detailed working knowledge of the powerful mathematical tools available to aid in data interpretation, especially when con fronted with large data sets incorporating many parameters. A minimal amount of com puter knowledge is necessary for successful applications, and we have tried conscien tiously to provide this in the appropriate sections and references. Scientific data are now being produced at rates not believed possible ten years ago. A major goal in any sci entific investigation should be to obtain a critical evaluation of the data generated in a set of experiments in order to extract whatever useful scientific information may be present. Very often, the large number of measurements present in the data set does not make this an easy task. The goals of this book are thus fourfold. The first is to create a useful reference on the applications of these statistical pattern recognition methods to the sciences. The majority of our discussions center around the fields of chemistry, geology, environmen tal sciences, physics, and the biological and medical sciences. In Chapter IV a section is devoted to each of these fields. Since the applications of pattern recognition tech niques are essentially unlimited, restricted only by the outer limitations of.

Pattern Recognition in Chemistry
Pattern Recognition in Chemistry

Author: Kurt Varmuza

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 232

ISBN-13: 3642931553

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Analytical chemistry of the recent years is strongly influenced by automation. Data acquisition from analytica~ instruments - and some times also controlling of instruments - by a computer are principally solved since many years. Availability of microcomputers made these tasks also feasible from the economic point of view. Besides these basic applications of computers in chemical measurements scientists developed computer programs for solving more sophisticated problems for which some kind of "intelligence" is usually supposed to be necessary. Harm less numerical experiments on this topic led to passionate discussions about the theme "which jobs cannot be done by a computer but only by human brain ?~. If this question is useful at all it should not be ans wered a priori. Application of computers in chemistry is a matter of utility, sometimes it is a social problem, but it is never a question of piety for the human brain. Automated instruments and the necessity to work on complex pro blems enhanced the development of automatic methods for the reduction and interpretation of large data sets. Numerous methods from mathematics, statistics, information theory, and computer science have been exten sively investigated for the elucidation of chemical information; a new discipline "chemometrics" has been established. Three different approaches have been used for computer-assisted interpretations of chemical data. 1. Heuristic methods try to formu late computer programs working in a similar way as a chemist would solve the problem. 2.

Methodologies of Pattern Recognition
Methodologies of Pattern Recognition

Author: Satosi Watanabe

Publisher: Academic Press

Published: 2014-05-12

Total Pages: 591

ISBN-13: 1483268985

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Methodologies of Pattern Recognition is a collection of papers that deals with the two approaches to pattern recognition (geometrical and structural), the Robbins-Monro procedures, and the implications of interactive graphic computers for pattern recognition methodology. Some papers describe non-supervised learning in statistical pattern recognition, parallel computation in pattern recognition, and statistical analysis as a tool to make patterns emerge from data. One paper points out the importance of cluster processing in visual perception in which proximate points of similar brightness values form clusters. At higher levels of mental activity humans are efficient in clumping complex items into clusters. Another paper suggests a recognition method which combines versatility and an efficient noise-proofness in dealing with the two main problems in the field of recognition. These difficulties are the presence of a large variety of observed signals and the presence of interference. One paper reports on a possible feature selection for pattern recognition systems employing the minimization of population entropy. Electronic engineers, physicists, physiologists, psychologists, logicians, mathematicians, and philosophers will find great rewards in reading the above collection.

Multivariate Pattern Recognition in Chemometrics
Multivariate Pattern Recognition in Chemometrics

Author: R.G. Brereton

Publisher: Elsevier

Published: 1992-09-04

Total Pages: 339

ISBN-13: 0080868363

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Chemometrics originated from multivariate statistics in chemistry, and this field is still the core of the subject. The increasing availability of user-friendly software in the laboratory has prompted the need to optimize it safely. This work comprises material presented in courses organized from 1987-1992, aimed mainly at professionals in industry. The book covers approaches for pattern recognition as applied, primarily, to multivariate chemical data. These include data reduction and display techniques, principal components analysis and methods for classification and clustering. Comprehensive case studies illustrate the book, including numerical examples, and extensive problems are interspersed throughout the text. The book contains extensive cross-referencing between various chapters, comparing different notations and approaches, enabling readers from different backgrounds to benefit from it and to move around chapters at will. Worked examples and exercises are given, making the volume valuable for courses. Tutorial versions of SPECTRAMAP and SIRIUS are optionally available as a Software Supplement, at a low price, to accompany the text.