Chen Sheng, Hong Xia, Luk Bing L, Harris Chris J
School of Electronics and Computer Science, University of Southampton, Southampton, UK
IEEE Trans Syst Man Cybern B Cybern. 2009 Apr;39(2):457-66. doi: 10.1109/TSMCB.2008.2006688. Epub 2008 Dec 16.
An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.
提出了一种基于留一法（LOO）准则的正交前向选择（OFS）算法，用于构建具有可调节点的径向基函数（RBF）网络。构建过程的每个阶段通过最小化留一法统计量来确定一个RBF节点，即其中心向量和对角协方差矩阵。对于回归应用，留一法准则选择为留一法均方误差，而在二类分类应用中采用留一法误分类率。这种OFS-LOO算法计算效率高，能够构建泛化性能良好的简约RBF网络。此外，该算法是完全自动的，用户无需为构建过程指定终止准则。通过回归和分类应用中的实例证明了所提出的RBF网络构建过程的有效性。
