Optimal Hyper-Parameter Search in Support Vector Machines Using Bézier Surfaces

We consider the problem of finding the optimal specification of hyper-parameters in Support Vector Machines (SVMs). We sample the hyper-parameter space and then use Bezier curves to approximate the performance surface. This geometrical approach allows us to use the information provided by the surface and find optimal specification of hyper-parameters. Our results show that in most cases the specification found by the proposed algorithm is very close to actual optimal point(s). The results suggest that our algorithm can serve as a framework for hyper-parameter search, which is precise and automatic.