Support vector machines and their application in chemistry and biotechnology [electronic resource] / Yizeng Liang ... [et al.].

Includes bibliographical references and index.

ch. 1. Overview of support vector machines -- ch. 2. Support vector machines for classification and regression -- ch. 3. Kernel methods -- ch. 4. Ensemble learning of support vector machines -- ch. 5. Support vector machines applied to near-infrared spectroscopy -- ch. 6. Support vector machines and QSAR/QSPR -- ch. 7. Support vector machines applied to traditional Chinese medicine -- ch. 8. Support vector machines applied to OMICS study.

"Support vector machines (SVMs), a promising machine learning method, is a powerful tool for chemical data analysis and for modeling complex physicochemical and biological systems. It is of growing interest to chemists and has been applied to problems in such areas as food quality control, chemical reaction monitoring, metabolite analysis, QSAR/QSPR, and toxicity. This book presents the theory of SVMs in a way that is easy to understand regardless of mathematical background. It includes simple examples of chemical and OMICS data to demonstrate the performance of SVMs and compares SVMs to other traditional classification/regression methods"-- Provided by publisher.

Also available in print edition.

Mode of access: World Wide Web.