Centre for Chemometrics, School of Chemistry, University of Bristol, Cantock's Close, Bristol, UK BS8 1TS.
Analyst. 2010 Feb;135(2):230-67. doi: 10.1039/b918972f. Epub 2009 Dec 23.
The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is shown with extensive visualisation, including learning machines, kernels and penalty functions. The influence of the penalty error and radial basis function radius on the model is illustrated. Multiclass implementations including one vs. all, one vs. one, fuzzy rules and Directed Acyclic Graph (DAG) trees are described. One-class Support Vector Domain Description (SVDD) is described and contrasted to conventional two- or multi-class classifiers. The use of Support Vector Regression (SVR) is illustrated including its application to multivariate calibration, and why it is useful when there are outliers and non-linearities.
描述了过去 15 年来人们对支持向量机（Support Vector Machines，SVMs）越来越感兴趣。使用模拟案例研究和 4 个实验案例研究来说明方法，即用于研究污染的质谱法、食品的近红外分析、聚合物的热分析和多环芳烃的紫外/可见光谱法。通过广泛的可视化，包括学习机、核函数和惩罚函数，展示了 SVMs 作为二类分类器的基础。说明了惩罚误差和径向基函数半径对模型的影响。描述了包括一对多、一对一、模糊规则和有向无环图（Directed Acyclic Graph，DAG）树在内的多类实现。描述了一类支持向量域描述（Support Vector Domain Description，SVDD），并将其与传统的二类或多类分类器进行了对比。说明了支持向量回归（Support Vector Regression，SVR）的使用，包括它在多元校准中的应用，以及为什么在存在离群值和非线性时它很有用。
