Table 4. Support vector machines.

Kernel Function	Linear		Quadratic		Polynomial		Gaussian Radial Basis function		Multilayer Perceptron Kernel
Method	Training	Test	Training	Test	Training	Test	Training	Test	Training	Test
Least Squares	35.43	54.75	0.68	44.08	0	45.85	0.21	49.08	35.58	49.61
Quadratic Programming	35.36	54.74	0.01	44.22	0	44.50	0.18	48.93	Fails to converge
Sequential Minimal Optimization	33.38	55.87	0.88	45.31	0	45.01	1.38	49.67	62.57	56.85

Training set and test set errors are shown for each combination of kernel function and method used to find the separating hyperplane. Training set errors as low as 0% were achieved with kernel functions of higher degrees and complexities, but test set errors were always >45%, indicating model over-fitting.