title: An empirical evaluation of the fuzzy kernel perceptron.
abstract: J.-H. Chen and C.-S. Chen have recently proposed a nonlinear variant of Keller and Hunt's fuzzy perceptron algorithm, based on the now familiar "kernel trick." In this letter, we demonstrate experimentally that J.-H. Chen and C.-S. Chen's assertion that the fuzzy kernel perceptron (FKP) outperforms the support vector machine (SVM) cannot be sustained. A more thorough model comparison exercise, based on a much wider range of benchmark data sets, shows that the FKP algorithm is not competitive with the SVM.
authors.forename: Gavin C
authors.initials: GC
authors.lastname: Cawley
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D017143:Fuzzy Logic; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*