Download or read book The Application of Pattern Recognition and Factor Analysis to Chemistry written by Michael James Vandaveer and published by . This book was released on 1980 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Chemical Pattern Recognition written by Oldřich Štrouf and published by Wiley-Blackwell. This book was released on 1986 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Pattern Recognition Approach to Data Interpretation written by Diane Wolff and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Multivariate Pattern Recognition in Chemometrics written by R.G. Brereton and published by Elsevier. This book was released on 1992-09-04 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling written by Jahan B. Ghasemi and published by Elsevier. This book was released on 2022-10-20 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book The Application of Pattern Recognition to Chemical Analysis written by Nels H. Mahle and published by . This book was released on 1974 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Application of Pattern Recognition to Mixed Chemical Exposure written by Barton Paul Simmons and published by . This book was released on 1991 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Contemporary Topics in Analytical and Clinical Chemistry written by David M. Hercules and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Chemical Applications of Pattern Recognition written by Peter C. Jurs and published by Wiley-Interscience. This book was released on 1975 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Application of Pattern Recognition Techniques to Data Derived from the Chemical Analysis of Common Wax Based Products and Ignitable Liquids written by Dzulkiflee Ismail and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition is a term that can be used to cover various stages of the investigation of characterising data sets including contributing to problem formulation and data collection through to discrimination, assessment and interpretation of results. Chemometrics techniques and Artificial Neural Networks (ANNs) are pattern recognition techniques commonly used to visualise and gather useful information from multidimensional datasets i.e. datasets with n-samples with m- variables. Of the many chemometric techniques available, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) are the most commonly used in the evaluation of dataset(s) generated from the analysis of samples which have relevance to forensic science. By contrast, Artificial Neural Networks (ANNs) and in particular Self Organising Feature Maps (SOFM) and Multi Layer Perceptron (MLP) have had limited application in forensic science eventhough these pattern recognition techniques have been known for almost 30 years. This study focuses on the applicability of the Artificial Neural Networks (ANNs) to specific datasets of forensic science interest and compares these with 'conventional' PCA and HCA techniques. Datasets generated from the analysis of wax based products and lighter fuels were used. The wax based product data set contained information obtained from Thin Layer Chromatography (TLC), Microspectrophotometry (MSP), Ultra-Violet and Visible Spectroscopy (UV/Vis) and Gas Chromatography with Flame Ionisation Detector (GC-FID) analysis of a variety of products from multiple sources where discrimination by brand was the objective. The data provided for the lighter fuel samples was obtained from analysis of a number of brands, both unevaporated and evaporated by Gas Chromatography-Mass Spectroscopy (GC-MS) and the objective was to discriminate the samples by brand as well as link degraded samples from the same brand together. The wax based product analysis provided simple, straight forward data whilst the lighter fuel analysis provided a more complicated and challenging dataset to investigate in terms of facilitating sample discrimination and/or linkage. In all cases, the 'conventional' Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) failed to provide any meaningful discrimination of the samples by product type regardless of the nature of the datasets. In contrast, the Artificial Neural Networks (ANNs) techniques provided full discrimination of the samples by product type even when the samples had undergone considerable ageing and weathering. This work has demonstrated the potential use of Self Organising Feature Maps (SOFM) and Multi Layer Perceptron (MLP) to datasets of forensic science relevance. The findings of this work provide avenues for further exploration of Artificial Neural Networks (ANNs) in forensic science.
Download or read book Chemical Applications of Pattern Recognition written by Peter C. Jurs and published by Wiley-Interscience. This book was released on 1975 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Proceedings of the Workshop on Chemical Applications of Pattern Recognition Washington D C May 1 2 1975 written by and published by . This book was released on 1976 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Chemometrics for Pattern Recognition written by Richard G. Brereton and published by John Wiley & Sons. This book was released on 2009-09-28 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Chemometrics in Environmental Chemistry Statistical Methods written by Jürgen Einax and published by Springer. This book was released on 2013-04-17 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition and other chemometrical techniques are important tools in interpreting environmental data. This volume presents authoritatively state-of-the-art procedures for measuring and handling environmental data. The chapters are written by leading experts.
Download or read book Application of Pattern Recognition to Catalytic Research written by Iosif Isaevich Ioffe and published by . This book was released on 1988 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Chemometrics for Pattern Recognition written by Richard G. Brereton and published by John Wiley & Sons. This book was released on 2009-06-29 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Pattern Recognition in Chemistry written by Kurt Varmuza and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
