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Fully supervised training of Gaussian radial basis function networks in WEKA

Abstract
Radial basis function networks are a type of feedforward network with a long history in machine learning. In spite of this, there is relatively little literature on how to train them so that accurate predictions are obtained. A common strategy is to train the hidden layer of the network using k-means clustering and the output layer using supervised learning. However, Wettschereck and Dietterich found that supervised training of hidden layer parameters can improve predictive performance. They investigated learning center locations, local variances of the basis functions, and attribute weights, in a supervised manner. This document discusses supervised training of Gaussian radial basis function networks in the WEKA machine learning software. More specifically, we discuss the RBFClassifier and RBFRegressor classes available as part of the RBFNetwork package for WEKA 3.7 and consider (a) learning of center locations and one global variance parameter, (b) learning of center locations and one local variance parameter per basis function, and (c) learning center locations with per-attribute local variance parameters. We also consider learning attribute weights jointly with other parameters.
Type
Report
Type of thesis
Series
Computer Science Working Papers
Citation
Frank, E. (2014). Fully supervised training of Gaussian radial basis function networks in WEKA (Computer Science Working Papers, 04/2014). Hamilton, NZ: Department of Computer Science, The University of Waikato.
Date
2014
Publisher
Department of Computer Science, The University of Waikato
Degree
Supervisors
Rights