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.
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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