Support vector machines and its applications in chemistry

Support vector machines (SVMs) are a promising machine learning method originally developed for pattern recognition problem based on structural risk minimization. Functionally, SVMs can be divided into two categories: support vector classification (SVC) machines and support vector regression (SVR) machines. According to this classification, their basic elements and algorithms are discussed in some detail and selected applications on two real world datasets and two simulated datasets are conducted to elucidate the good generalization performance of SVMs, specially good for treating the data of some nonlineartiy.