Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd floor, 14074-Córdoba, Spain.
Neural Netw. 2011 Sep;24(7):779-84. doi: 10.1016/j.neunet.2011.03.014. Epub 2011 Mar 21.
This paper proposes a radial basis function neural network (RBFNN), called the q-Gaussian RBFNN, that reproduces different radial basis functions (RBFs) by means of a real parameter q. The architecture, weights and node topology are learnt through a hybrid algorithm (HA). In order to test the overall performance, an experimental study with sixteen data sets taken from the UCI repository is presented. The q-Gaussian RBFNN was compared to RBFNNs with Gaussian, Cauchy and inverse multiquadratic RBFs in the hidden layer and to other probabilistic classifiers, including different RBFNN design methods, support vector machines (SVMs), a sparse classifier (sparse multinomial logistic regression, SMLR) and a non-sparse classifier (regularized multinomial logistic regression, RMLR). The results show that the q-Gaussian model can be considered very competitive with the other classification methods.
本文提出了一种径向基函数神经网络（RBFNN），称为 q-高斯 RBFNN，它通过实参数 q 再现不同的径向基函数（RBF）。通过混合算法（HA）学习架构、权重和节点拓扑。为了测试整体性能，提出了一个基于来自 UCI 存储库的 16 个数据集的实验研究。q-高斯 RBFNN 与在隐藏层中具有高斯、柯西和逆多二次 RBF 的 RBFNN 以及其他概率分类器进行了比较，包括不同的 RBFNN 设计方法、支持向量机（SVM）、稀疏分类器（稀疏多项逻辑回归，SMLR）和非稀疏分类器（正则化多项逻辑回归，RMLR）。结果表明，q-高斯模型可以被认为与其他分类方法具有很强的竞争力。
