Support Vector Machine in Chemistry

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...
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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.
Contents: Support Vector MachineKernel FunctionsFeature Selection Using Support Vector MachinePrinciple of Atomic or Molecular Parameter–Data Processing MethodSVM Applied to Phase Diagram Assessment and PredictionSVM Applied to Thermodynamic Property PredictionSVM Applied to Molecular and Materials DesignSVM Applied to Structure–Activity RelationshipsSVM Applied to Data of Trace Element AnalysisSVM Applied to Archeological Chemistry of Ancient CeramicsSVM Applied to Cancer ResearchSVM Applied to Some Topics of Chemical AnalysisSVM Applied to Chemical and Metallurgical Technology
Readership: Undergraduates, graduate students, and researchers in computational chemistry.