The Support Vector Machine (SVM) has been a popular choice of classification method for many applications in machine learning. Despite its competitive classification accuracy, the implicit nature of the solution renders the SVM less attractive in providing insights into the relationship between covariates and classes. To enhance interpretability of the SVM, we integrate feature selection into the general setup of nonlinear SVM for multiclass case. Simultaneous feature selection and prediction is accomplished by functional ANOVA decomposition and the L_1 penalty imposed on the scale parameters of functional subspaces. In addition, characterization of the solution path of the multicategory SVM is discussed as a computational shortcut to attain the entire spectrum of solutions from the most regularized to the completely overfitted ones.