number of dimensions is greater than the number of samples they use a subset of training points in the decision function or support vectors so it's also memory efficient suppose factors are first down so different kernels can be specified for the decision function common kernels are provided but it's also possible to specify custom kernels we can add kernel functions together to achieve even more complex hyperplanes the disadvantages however of sports vector machines include if the number of features is greater than the number of samples the