Title :
Learning heterogeneous functions from sparse and non-uniform samples
Author :
Pokrajac, Dragoljub ; Obradovic, Zoran
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA
Abstract :
A boosting-based method for centers placement in radial basis function networks (RBFNs) is proposed. Also, the influence of several methods for drawing random samples on the accuracy of RBFNs is examined. The new method is compared to trivial, linear and non-linear regressors including the multilayer perceptron and alternative RBFN learning algorithms and its advantages are demonstrated for learning heterogeneous functions from sparse and non-uniform samples
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); radial basis function networks; boosting-based method; centers placement; heterogeneous functions; learning algorithms; linear regressors; multilayer perceptron; nonlinear regressors; nonuniform samples; sparse samples; trivial regressors; Boosting; Computer science; Multilayer perceptrons; Neurons; Predictive models; Probability distribution; Radial basis function networks; Regression tree analysis; Sampling methods; Training data;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861288
