Description: 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.
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File list (Check if you may need any files):
demofun\changelssvm.m
.......\demofun.m
.......\initlssvm.m
.......\kernel_matrix.m
.......\lssvmMATLAB.m
.......\plotlssvm.m
.......\postlssvm.m
.......\prelssvm.m
.......\simlssvm.m
.......\trainlssvm.m
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