A Hybrid Learning Algorithm for Elliptical Basis Function Neural Networks
XING HongJie1,2,3, WANG Yong1,2, HU BaoGang1,2
1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080 2. Graduate School, Chinese Academy of Sciences, Beijing 1000803. 3.College of Mathematics and Computer Science, Hebei University, Baoding 071002
Abstract：A hybrid learning method for the elliptical basis function neural network (EBFNN) is presented. Firstly, the parameters of elliptical basis function (EBF) units in the hidden layer of the EBFNN are initialized by the expectationmaximization (EM) algorithm, while the connection weights plus bias term is initialized by the linear leastsquared method. Then, the gradient descent based optimization procedure adjusts all the parameters simultaneously. The comparison results show that the gradient descent elliptical basis function neural network (GDEBFNN) trained by the proposed hybrid learning method upon the test datasets has higher accuracy than the other three related models. Compared with support vector machine (SVM), the GDEBFNN can achieve comparable generalization ability. Moreover, the GDEBFNN obtains better generalization performance than the decision tree constructed by the Adaboost method.
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2008, Vol. 21 
