Low-cost model selection for SVMs using local features

Many practical engineering applications require the usage of accurate automatic decision systems, usually operating under tight computational constraints. Support Vector Machines (SVMs) endowed with a Radial Basis Function (RBF) as kernel are broadly accepted as the current state of the art for decision problems, but require cross-validation to select the free parameters, which is computationally costly. In this work we investigate low-cost methods to select the spread parameter in SVMs with an RBF kernel. Our proposal relies on the use of simple local methods that gather information about the local structure of each dataset. Empirical results in UCI datasets show that the proposed methods can be used as a fast alternative to the standard cross-validation procedure, with the additional advantage of avoiding the (often heuristic) task of a priori fixing the values of the spread parameter to be explored.