Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling PDF Download
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Author: Jahan B. Ghasemi
Publisher: Elsevier
ISBN: 0323907067
Category : Science
Languages : en
Pages : 212
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Book Description
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
Author: Jahan B. Ghasemi
Publisher: Elsevier
ISBN: 0323907067
Category : Science
Languages : en
Pages : 212
Get Book Here
Book Description
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
Author: Nusrat Parveen
Publisher: Mohammed Abdul Malik
ISBN:
Category : Science
Languages : en
Pages : 0
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Book Description
Modeling of any system or a process is one of the significant but challenging tasks in engineering. The challenge is either due to the physical complexity of natural phenomenon or our limited knowledge of mathematics. Recently, data driven modeling (DDM) has been found to be a very powerful tool in helping to overcome those challenges, by presenting opportunities to build basic models from the observed patterns as well as accelerating the response of decision makers in facing real world problems. Since DDM is able to map causal factors and consequent outcomes from the observed patterns (experimental data), without deep knowledge of the complex physical process, these modeling techniques are becoming popular among engineers. Soft computing and statistical models are the two commonly employed data-driven models for predictive modeling. As far as the statistical data-driven models are concerned, these models could be employed in the life of modern engineering. But the accuracy and generalizability of these models is very poor. The soft computing data- driven modeling techniques have attracted the attention of many researchers across the globe to overcome the limitations of statistical methods. The statistical data-driven modeling techniques such as least-squares methods, the maximum likelihood methods and traditional artificial neural network (ANN) are based on empirical risk minimization (ERM) principle while the support vector machine (SVM) method is based on the structural risk minimization (SRM) principle. According to it, the generalization accuracy is optimized over the empirical error and the flatness of the regression function or the capacity of SVM. On the other hand, the ANN and other traditional regression models which are based on ERM principle minimize the empirical error and do not consider the capacity of the learning machines. This results in model over fitting i.e. high prediction accuracy for the training data set and low for the test (unseen) data, giving poor generalization performance. SVMs belong to the supervised machine learning theory and are applied to both nonlinear classification called support vector classification (SVC) and regression or SVR. SVM possesses many advantages over traditional neural networks.
Author: Hachmann
Publisher: Wiley-Blackwell
ISBN: 9781119310914
Category :
Languages : en
Pages : 524
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Book Description
Author: Takashiro Akitsu
Publisher: Elsevier
ISBN: 0128232722
Category : Science
Languages : en
Pages : 280
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Book Description
Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications highlights fundamental knowledge and current developments in the field, giving readers insight into how these tools can be harnessed to enhance their own work. Offering the ability to process large or complex data-sets, compare molecular characteristics and behaviors, and help researchers design or identify new structures, Artificial Intelligence (AI) holds huge potential to revolutionize the future of chemistry. Volume 1 explores the fundamental knowledge and current methods being used to apply AI across a whole host of chemistry applications. Drawing on the knowledge of its expert team of global contributors, the book offers fascinating insight into this rapidly developing field and serves as a great resource for all those interested in exploring the opportunities afforded by the intersection of chemistry and AI in their own work. Part 1 provides foundational information on AI in chemistry, with an introduction to the field and guidance on database usage and statistical analysis to help support newcomers to the field. Part 2 then goes on to discuss approaches currently used to address problems in broad areas such as computational and theoretical chemistry; materials, synthetic and medicinal chemistry; crystallography, analytical chemistry, and spectroscopy. Finally, potential future trends in the field are discussed. - Provides an accessible introduction to the current state and future possibilities for AI in chemistry - Explores how computational chemistry methods and approaches can both enhance and be enhanced by AI - Highlights the interdisciplinary and broad applicability of AI tools across a wide range of chemistry fields
Author: Dongda Zhang
Publisher: Royal Society of Chemistry
ISBN: 1839165634
Category : Science
Languages : en
Pages : 441
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Book Description
Author: R.G. Brereton
Publisher: Elsevier
ISBN: 0080868363
Category : Science
Languages : en
Pages : 339
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Book Description
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.
Author: Richard G. Brereton
Publisher: John Wiley & Sons
ISBN: 0470987251
Category : Science
Languages : en
Pages : 532
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Book Description
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.
Author: Georg Langs
Publisher: Springer
ISBN: 3642347134
Category : Computers
Languages : en
Pages : 277
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Book Description
Brain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. This state-of-the-art survey offers a collection of papers from the Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning.
Author: Jon Paul Janet
Publisher: American Chemical Society
ISBN: 0841299005
Category : Science
Languages : en
Pages : 189
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Book Description
Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemistsderivations where they are important
Author: Dirk P. Kroese
Publisher: CRC Press
ISBN: 1000730778
Category : Business & Economics
Languages : en
Pages : 538
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Book Description
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
