Advantages associated to SVMs:
Effective in high dimensional spaces. Still effective in cases where number of dimensions is greater than the number of samples. Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. It is versatile as different Kernel Functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.