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

Support Vector Machines and Their Application in Chemistry and Biotechnology (eBook, PDF)

• Format: PDF

Produktbeschreibung

Presenting a clear bridge between theory and application, this volume provides a thorough description of the mechanism of support vector machines (SVMs) from the point of view of chemists and biologists. Topics discussed include elements and algorithms of support vector classification (SVC) machines and support vector regression machines (SVR), the kernel function for solving nonlinear problems, ensemble learning of SVMs, applications of SVMs to near-infrared data, SVMs and quantitative structure-activity/property relationships (QSAR/QSPR), quality control of traditional Chinese medicine by means of the chromatography fingerprint technique, and the use of SVMS in exploring the biological data produced in OMICS study.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 211
• Erscheinungstermin: 19. April 2016
• Englisch
• ISBN-13: 9781439821282
• Artikelnr.: 38303145

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.