Radial basis function neural networks for nonlinear Fisher discrimination and Neyman-Pearson classification
Radial basis function neural networks for nonlinear Fisher discrimination and Neyman-Pearson classification
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DOI：
10.1016/s0893-6080(03)00086-8
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发表时间：
2003-06-01
期刊：
影响因子：
7.8
通讯作者：
Chen, XW
中科院分区：
文献类型：
--
作者：
Casasent, D;Chen, XW
提出了一种新的径向基函数（RBF）神经网络（NN）的设计方法。为了选择不同的RBF参数，利用训练样本的类隶属度信息产生新的聚类类。这允许强调某些类数据的分类性能，而不是最佳整体分类。这使我们能够根据需要控制性能并近似Neyman-Pearson分类。我们还表明，通过适当地选择所需的输出神经元的水平，然后隐藏到输出层的RBF进行Fisher判别分析，整个系统进行非线性Fisher分析。农产品检验问题的数据和合成数据证实了这些方法的有效性。(C)2003爱思唯尔科技有限公司版权所有。
We propose a novel technique for the design of radial basis function (RBF) neural networks (NNs). To select various RBF parameters, the class membership information of training samples is utilized to produce new cluster classes. This allows emphasis of classification performance for certain class data rather than best overall classification. This allows us to control performance as desired and to approximate Neyman-Pearson classification. We also show that by properly choosing the desired output neuron levels, then the RBF hidden to output layer performs Fisher discrimination analysis, and that the full system performs a nonlinear Fisher analysis. Data on an agricultural product inspection problem and on synthetic data confirm the effectiveness of these methods. (C) 2003 Elsevier Science Ltd. All rights reserved.