کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
395357 | 665954 | 2009 | 19 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Radial Basis Function network learning using localized generalization error bound Radial Basis Function network learning using localized generalization error bound](/preview/png/395357.png)
Training a classifier with good generalization capability is a major issue for pattern classification problems. A novel training objective function for Radial Basis Function (RBF) network using a localized generalization error model (L-GEM) is proposed in this paper. The localized generalization error model provides a generalization error bound for unseen samples located within a neighborhood that contains all training samples. The assumption of the same width for all dimensions of a hidden neuron in L-GEM is relaxed in this work. The parameters of RBF network are selected via minimization of the proposed objective function to minimize its localized generalization error bound. The characteristics of the proposed objective function are compared with those for regularization methods. For weight selection, RBF networks trained by minimizing the proposed objective function consistently outperform RBF networks trained by minimizing the training error, Tikhonov Regularization, Weight Decay or Locality Regularization. The proposed objective function is also applied to select center, width and weight in RBF network simultaneously. RBF networks trained by minimizing the proposed objective function yield better testing accuracies when compared to those that minimizes training error only.
Journal: Information Sciences - Volume 179, Issue 19, 9 September 2009, Pages 3199–3217