Yizeng Liang, Qing-Song Xu, Hong-Dong Li, Dong-Sheng Cao

Support Vector Machines and Their Application in Chemistry and Biotechnology

• Gebundenes Buch

Produktbeschreibung

"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"--

Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 212
• Erscheinungstermin: 9. Mai 2011
• Englisch
• Abmessung: 247mm x 161mm x 18mm
• Gewicht: 427g
• ISBN-13: 9781439821275
• ISBN-10: 1439821275
• Artikelnr.: 31963770

Autorenporträt

Yizeng Liang and Qing-Song Xu are with Central South University in Changsha, China.

Inhaltsangabe

Overview of support vector machines. Support vector machines for
classification and regression. Kernel methods. Ensemble learning of support
vector machines. Support vector machines applied to near-infrared
spectroscopy. Support vector machines and QSAR/QSPR. Support vector
machines applied to traditional Chinese medicine. Support vector machines
applied to OMICS study. Index.