Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar wi
Autorentext
Yizeng Liang and Qing-Song Xu are with Central South University in Changsha, China.
Inhalt
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.