Analysis of Sensitivity to the Kernel Parameter Choice: Comparing the Performance Profiles Exhibited by Standard andLeast-Squares SVM Classifiers

Support vector machines (SVMs) have established themselves as a state-of-the-art technique for coping with non-trivial machine learning problems. Among the SVM variants, least-squares SVMs have gained increased attention recently due to the computational benefits they usually entail. Although considered as high-performance models, it is consensual that the applicability of these vector machines depends very much on a proper choice of some control parameters. In this paper, we present a sensitivity analysis study contrasting the performance profiles exhibited by standard and least-squares SVM classifiers with respect to the calibration of the kernel parameter value alone. The results achieved with simulations involving seven datasets indicate that the performance profiles are usually qualitatively similar for the two types of vector machines, both presenting kernel parameter values clearly associated with a better performance, and that the choice of the kernel function seems to be more critical than that of its parameter value.