模糊核感知器的实证评估。

An empirical evaluation of the fuzzy kernel perceptron.

作者信息

Cawley Gavin C

出版信息

IEEE Trans Neural Netw. 2007 May;18(3):935-7. doi: 10.1109/TNN.2007.891624.

DOI:10.1109/TNN.2007.891624

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.

摘要

J.-H. 陈和C.-S. 陈最近基于现在广为人知的“核技巧”提出了凯勒和亨特模糊感知器算法的一种非线性变体。在这封信中，我们通过实验证明J.-H. 陈和C.-S. 陈关于模糊核感知器（FKP）优于支持向量机（SVM）的断言是站不住脚的。基于更广泛的基准数据集进行的更全面的模型比较实验表明，FKP算法与SVM相比没有竞争力。

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