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Support Vector Machine In Chemistry
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Book Synopsis Support Vector Machines and Their Application in Chemistry and Biotechnology by : Yizeng Liang
Download or read book Support Vector Machines and Their Application in Chemistry and Biotechnology written by Yizeng Liang and published by CRC Press. This book was released on 2016-04-19 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar wi
Book Synopsis Support Vector Machine in Chemistry by : Nianyi Chen
Download or read book Support Vector Machine in Chemistry written by Nianyi Chen and published by World Scientific. This book was released on 2004 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, a new method of data processing using the support vector machine (SVM) has been introduced to the field of chemistry. Compared with other methods of data processing, the SVM has the advantage of good prediction reliability. It is especially suitable for small sample sizes, such as in chemical research on QSAR/QSPR work, materials and experimental design, phase diagram prediction, etc. The SVM is fast becoming a useful tool for chemists. This book provides a systematic approach to the principles and algorithms of the SVM, and looks at its application in many branches of chemistry.
Book Synopsis Support Vector Machine in Chemistry by : Nianyi Chen
Download or read book Support Vector Machine in Chemistry written by Nianyi Chen and published by World Scientific. This book was released on 2004 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, a new method of data processing using the support vector machine (SVM) has been introduced to the field of chemistry. Compared with other methods of data processing, the SVM has the advantage of good prediction reliability. It is especially suitable for small sample sizes, such as in chemical research on QSAR/QSPR work, materials and experimental design, phase diagram prediction, etc. The SVM is fast becoming a useful tool for chemists. This book provides a systematic approach to the principles and algorithms of the SVM, and looks at its application in many branches of chemistry.
Book Synopsis Support-vector Machines by : Pooja Saigal
Download or read book Support-vector Machines written by Pooja Saigal and published by Nova Science Publishers. This book was released on 2020-12-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Support Vector Machines: Evolution and Applications reviews the basics of Support Vector Machines (SVM), their evolution and applications in diverse fields. SVM is an efficient supervised learning approach popularly used for pattern recognition, medical image classification, face recognition and various other applications. In the last 25 years, a lot of research has been carried out to extend the use of SVM to a variety of domains. This book is an attempt to present the description of a conventional SVM, along with discussion of its different versions and recent application areas. The first chapter of this book introduces SVM and presents the optimization problems for a conventional SVM. Another chapter discusses the journey of SVM over a period of more than two decades. SVM is proposed as a separating hyperplane classifier that partitions the data belonging to two classes. Later on, various versions of SVM are proposed that obtain two hyperplanes instead of one. A few of these variants of SVM are discussed in this book. The major part of this book discusses some interesting applications of SVM in areas like quantitative diagnosis of rotor vibration process faults through power spectrum entropy-based SVM, hardware architectures of SVM applied in pattern recognition systems, speaker recognition using SVM, classification of iron ore in mines and simultaneous prediction of the density and viscosity for the ternary system water- ethanol-ethylene glycol ionic liquids. The latter part of the book is dedicated to various approaches for the extension of SVM and similar classifiers to a multi-category framework, so that they can be used for the classification of data with more than two classes"--
Book Synopsis Encyclopedia of Physical Organic Chemistry, 6 Volume Set by : Zerong Wang
Download or read book Encyclopedia of Physical Organic Chemistry, 6 Volume Set written by Zerong Wang and published by John Wiley & Sons. This book was released on 2017-04-17 with total page 4542 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of 2018 PROSE Award for MULTIVOLUME REFERENCE/SCIENCE This encyclopedia offers a comprehensive and easy reference to physical organic chemistry (POC) methodology and techniques. It puts POC, a classical and fundamental discipline of chemistry, into the context of modern and dynamic fields like biochemical processes, materials science, and molecular electronics. Covers basic terms and theories into organic reactions and mechanisms, molecular designs and syntheses, tools and experimental techniques, and applications and future directions Includes coverage of green chemistry and polymerization reactions Reviews different strategies for molecular design and synthesis of functional molecules Discusses computational methods, software packages, and more than 34 kinds of spectroscopies and techniques for studying structures and mechanisms Explores applications in areas from biology to materials science The Encyclopedia of Physical Organic Chemistry has won the 2018 PROSE Award for MULTIVOLUME REFERENCE/SCIENCE. The PROSE Awards recognize the best books, journals and digital content produced by professional and scholarly publishers. Submissions are reviewed by a panel of 18 judges that includes editors, academics, publishers and research librarians who evaluate each work for its contribution to professional and scholarly publishing. You can find out more at: proseawards.com Also available as an online edition for your library, for more details visit Wiley Online Library
Book Synopsis Knowledge Discovery with Support Vector Machines by : Lutz H. Hamel
Download or read book Knowledge Discovery with Support Vector Machines written by Lutz H. Hamel and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
Book Synopsis EEG Brain Signal Classification for Epileptic Seizure Disorder Detection by : Sandeep Kumar Satapathy
Download or read book EEG Brain Signal Classification for Epileptic Seizure Disorder Detection written by Sandeep Kumar Satapathy and published by Academic Press. This book was released on 2019-02-10 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification. - Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures - Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers - Provides a number of experimental analyses, with their results discussed and appropriately validated
Book Synopsis Machine Learning in Chemistry by : Hugh M. Cartwright
Download or read book Machine Learning in Chemistry written by Hugh M. Cartwright and published by Royal Society of Chemistry. This book was released on 2020-07-15 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.
Download or read book Pattern Recognition written by Axel Pinz and published by Springer. This book was released on 2012-08-14 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 34th Symposium of the German Association for Pattern Recognition, DAGM 2012, and the 36th Symposium of the Austrian Association for Pattern Recognition, OAGM 2012, held in Graz, Austria, in August 2012. The 27 revised full papers and 23 revised poster papers were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on segmentation, low-level vision, 3D reconstruction, recognition, applications, learning, and features.
Book Synopsis Handbook of Neural Computation by : Pijush Samui
Download or read book Handbook of Neural Computation written by Pijush Samui and published by Academic Press. This book was released on 2017-07-18 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. - Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more - Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing - Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods
Book Synopsis Rule Extraction from Support Vector Machines by : Joachim Diederich
Download or read book Rule Extraction from Support Vector Machines written by Joachim Diederich and published by Springer. This book was released on 2007-12-27 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made. This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts.
Book Synopsis Support Vector Machine In Chemistry by : Nianyi Chen
Download or read book Support Vector Machine In Chemistry written by Nianyi Chen and published by World Scientific. This book was released on 2004-08-23 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, the support vector machine (SVM), a new data processing method, has been applied to many fields of chemistry and chemical technology. Compared with some other data processing methods, SVM is especially suitable for solving problems of small sample size, with superior prediction performance. SVM is fast becoming a powerful tool of chemometrics. This book provides a systematic approach to the principles and algorithms of SVM, and demonstrates the application examples of SVM in QSAR/QSPR work, materials and experimental design, phase diagram prediction, modeling for the optimal control of chemical industry, and other branches in chemistry and chemical technology.
Book Synopsis Machine Learning in Chemistry by : Jon Paul Janet
Download or read book Machine Learning in Chemistry written by Jon Paul Janet and published by American Chemical Society. This book was released on 2020-05-28 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Bioinformatics for Geneticists by : Michael R. Barnes
Download or read book Bioinformatics for Geneticists written by Michael R. Barnes and published by John Wiley & Sons. This book was released on 2003-07-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely book illustrates the value of bioinformatics, not simply as a set of tools but rather as a science increasingly essential to navigate and manage the host of information generated by genomics and the availability of completely sequenced genomes. Bioinformatics can be used at all stages of genetics research: to improve study design, to assist in candidate gene identification, to aid data interpretation and management and to shed light on the molecular pathology of disease-causing mutations. Written specifically for geneticists, this book explains the relevance of bioinformatics showing how it may be used to enhance genetic data mining and markedly improve genetic analysis.
Book Synopsis Chemistry of Petrochemical Processes by : Sami Matar Ph.D.
Download or read book Chemistry of Petrochemical Processes written by Sami Matar Ph.D. and published by Elsevier. This book was released on 2001-07-26 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Chemistry of Petrochemical Processes, readers find a handy and valuable source of information containing insights into petrochemical reactions and products, process technology, and polymer synthesis. The book reviews and describes the reactions and processes involved in transforming petroleum-based hydrocarbons into the chemicals that form the basis of the multi-billion dollar petrochemical industry. In addition, the book includes information on new process developments for the production of raw materials and intermediates for petrochemicals that have surfaced since the book's first edition. - Provides a quick understanding of the chemical reactions associated with oil and gas processing - Contains insights into petrochemical reactions and products, process technology, and polymer synthesis
Book Synopsis Pacific Symposium on Biocomputing 2002 by : Russ B. Altman
Download or read book Pacific Symposium on Biocomputing 2002 written by Russ B. Altman and published by World Scientific. This book was released on 2001 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Pacific Symposium on Biocomputing brings together key researchers from the international biocomputing community. It is designed to be maximally responsive to the need for critical mass in subdisciplines within biocomputing. This book contains peer-reviewed articles in computational biology.
Book Synopsis Learning with Support Vector Machines by : Colin Campbell
Download or read book Learning with Support Vector Machines written by Colin Campbell and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels
