Title :
An Empirical Evaluation of the Fuzzy Kernel Perceptron
Author_Institution :
Sch. of Comput. Sci., East Anglia Univ., Norwich
fDate :
5/1/2007 12:00:00 AM
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
Keywords :
fuzzy neural nets; fuzzy set theory; support vector machines; fuzzy kernel perceptron; kernel trick; nonlinear fuzzy perceptron algorithm; support vector machine; Benchmark testing; Error analysis; Fuzzy sets; Ionosphere; Kernel; Performance evaluation; Sonar; Spirals; Stochastic processes; Support vector machines; Fuzzy sets; kernel machines; Algorithms; Artificial Intelligence; Computer Simulation; Decision Support Techniques; Fuzzy Logic; Information Storage and Retrieval; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.891624
