This a simple demo, solving a simple regression task using LS-SVMlab. A dataset is constructed in the right formatting. The data are represented as matrices where each row contains one datapoint. In order to make an LS-SVM model, we need 2 extra parameters: gamma (gam) is the regularization parameter, determining the trade-off between the fitting error minimization and smoothness of the estimated function. sigma^2 (sig2) is the kernel function parameter of the RBF kernel. The parameters and the variables relevant for the LS-SVM are passed as one cell. This cell allows for consistent default handling of LS-SVM parameters and syntactical grouping of related arguments. -This is a simple demo, solving a simple regression task using LS-SVMlab. A dataset is constructed in the right formatting. The data are represented as matrices where each row contains one datapoint. In order to make an LS-SVM model, we need 2 extra parameters: gamma (gam) is the regularization parameter, determining the trade-off between the fitting error minimization and smoothness of the estimated function. sigma^2 (sig2) is the kernel function parameter of the RBF kernel. The parameters and the variables relevant for the LS-SVM are passed as one cell. This cell allows for consistent default handling of LS-SVM parameters and syntactical grouping of related arguments.