Author Description

<p>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 with what they are and how they work. Presenting a clear bridge between theory and application, Support Vector Machines and Their Application in Chemistry and Biotechnology provides a thorough description of the mechanism of SVMs from the point of view of chemists and biologists, enabling them to solve difficult problems with the help of these powerful tools.</p> <p>Topics discussed include:</p> <ul> <li>Background and key elements of support vector machines and applications in chemistry and biotechnology</li> <li>Elements and algorithms of support vector classification (SVC) and support vector regression (SVR) machines, along with discussion of simulated datasets</li> <li>The kernel function for solving nonlinear problems by using a simple linear transformation method</li> <li>Ensemble learning of support vector machines</li> <li>Applications of support vector machines to near-infrared data</li> <li>Support vector machines and quantitative structure-activity/property relationship (QSAR/QSPR)</li> <li>Quality control of traditional Chinese medicine by means of the chromatography fingerprint technique</li> <li>The use of support vector machines in exploring the biological data produced in OMICS study</li> </ul> <p>Beneficial for chemical data analysis and the modeling of complex physic-chemical and biological systems, support vector machines show promise in a myriad of areas. This book enables non-mathematicians to understand the potential of SVMs and utilize them in a host of applications.</p>