COMBINING REGRESSION TREES AND RADIAL BASIS FUNCTION NETWORKS

2000 ◽  
Vol 10 (06) ◽  
pp. 453-465 ◽  
Author(s):  
MARK ORR ◽  
JOHN HALLAM ◽  
KUNIO TAKEZAWA ◽  
ALAN MURRAY ◽  
SEISHI NINOMIYA ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Regression Trees ◽  
Radial Basis Function Networks ◽  
Data Sets ◽  
Parametric Regression ◽  
Real World Problem ◽  
Radial Basis ◽  
Soybean Plants

We describe a method for non-parametric regression which combines regression trees with radial basis function networks. The method is similar to that of Kubat,1 who was first to suggest such a combination, but has some significant improvements. We demonstrate the features of the new method, compare its performance with other methods on DELVE data sets and apply it to a real world problem involving the classification of soybean plants from digital images.

scholarly journals Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yunfeng Wu ◽  
Xin Luo ◽  
Fang Zheng ◽  
Shanshan Yang ◽  
Suxian Cai ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Function Approximation ◽  
Mean Squared Error ◽  
Weighted Average ◽  
Radial Basis Function Networks ◽  
Data Sets ◽  
Radial Basis ◽  
Synthetic Function ◽  
Adaptively Adjusting

This paper presents a novel adaptive linear and normalized combination (ALNC) method that can be used to combine the component radial basis function networks (RBFNs) to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error) and the better fidelity (characterized by normalized correlation coefficient) of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

Indexed Families of Functionals and Gaussian Radial Basis Functions

2003 ◽  
Vol 15 (2) ◽  
pp. 455-468 ◽  
Author(s):  
Irwin W. Sandberg
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Basis Functions ◽  
Inner Product ◽  
Radial Basis Function Networks ◽  
Product Spaces ◽  
Inner Product Spaces ◽  
Finite Dimensional ◽  
Radial Basis

We report on results concerning the capabilities of gaussian radial basis function networks in the setting of inner product spaces that need not be finite dimensional. Specifically, we show that important indexed families of functionals can be uniformly approximated, with the approximation uniform also with respect to the index. Applications are described concerning the classification of signals and the synthesis of reconfigurable classifiers.

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