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.
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First Sentence:
Carrying out experimental work, finding the regularities of the data obtained, and making prediction for some unknown phenomena, are the chief mode of the research work in the fields of chemistry and related disciplines, including chemical engineering, materials science and environmental science. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
bad sample points, official kiln, good sample points, support vector classification, multitask learning, chemical data processing, electrochemical capacity, linear kernel function, support vector regression, industrial optimization, geometric margin, nonlinear data sets, computerized prediction, floating search method, quadratic data, glaze samples, functional margin, hypothetical solubilities, molten salt systems, porcelain chips, melting types, outlier elimination, porcelain kilns, data processing work, nontransition metals Key Phrases - Capitalized Phrases (CAPs): (learn more)
Extra Features, Main Task, Selected Inputs, Radius Ion, Ming Dynasty, Some Topics of Chemical Analysis, Data of Trace Element Analysis, Element Covalent, Log Koc New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Carrying out experimental work, finding the regularities of the data obtained, and making prediction for some unknown phenomena, are the chief mode of the research work in the fields of chemistry and related disciplines, including chemical engineering, materials science and environmental science. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
bad sample points, official kiln, good sample points, support vector classification, multitask learning, chemical data processing, electrochemical capacity, linear kernel function, support vector regression, industrial optimization, geometric margin, nonlinear data sets, computerized prediction, floating search method, quadratic data, glaze samples, functional margin, hypothetical solubilities, molten salt systems, porcelain chips, melting types, outlier elimination, porcelain kilns, data processing work, nontransition metals Key Phrases - Capitalized Phrases (CAPs): (learn more)
Extra Features, Main Task, Selected Inputs, Radius Ion, Ming Dynasty, Some Topics of Chemical Analysis, Data of Trace Element Analysis, Element Covalent, Log Koc New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 7
books:
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- Statistical Learning Theory by Vladimir N. Vapnik on page 19, and Back Matter
- Adaptive Control Processes by Bellman in Back Matter
- Practical Methods of Optimization by R. Fletcher in Back Matter
- Applied Regression Analysis: A Research Tool (Springer Texts in Statistics) by John O. Rawlings in Back Matter
- Mathematical Programming: Theory and Algorithms (Wiley-Interscience series in discrete mathematics and optimization) by Michel Minoux in Back Matter
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