April 2016
Intermediate to advanced
211 pages
6h 31m
English
book
Support Vector Machines and Their Application in Chemistry and Biotechnology
by Yizeng Liang, Qing-Song Xu, Hong-Dong Li, Dong-Sheng Cao
Overview
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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