A Nonmathematical Introduction to SVM

The principal characteristics of the SVM models are presented here in a nonmathematical way and examples of SVM applications to classification and regression problems are given in this section. The mathematical basis of SVM will be presented in subsequent sections of this tutorial/review chapter. 

We will use this dataset later to demonstrate the kernel influence on the SVM regression, as well as the effect of modifying the tube radius e. However, we will not present QSAR statistics for the SVM model. Comparative QSAR models are shown in the section on SVM applications in chemistry. 

The SVMR experiments that we have just carried out using the QSAR dataset from Table 2 offer convincing proof for the SVM ability to model nonlinear relationships but also their overfitting capabilities. This dataset was presented only for demonstrative purposes, and we do not recommend the use of SVM for QSAR models with such a low number of compounds and descriptors. 

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