by Nianyi Chen (Shanghai University, China) , Wencong Lu (Shanghai University, China) , Jie Yang (Shanghai Jiao Tong University, China) , & Guozheng Li (Shanghai Jiao Tong University, China)

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.

• Support Vector Machine
• Kernel Functions
• Feature Selection Using Support Vector Machine
• Principle of Atomic or Molecular Parameter–Data Processing Method
• SVM Applied to Phase Diagram Assessment and Prediction
• SVM Applied to Thermodynamic Property Prediction
• SVM Applied to Molecular and Materials Design
• SVM Applied to Structure–Activity Relationships
• SVM Applied to Data of Trace Element Analysis
• SVM Applied to Archeological Chemistry of Ancient Ceramics
• SVM Applied to Cancer Research
• SVM Applied to Some Topics of Chemical Analysis
• SVM Applied to Chemical and Metallurgical Technology