Waikato University crest

Department of
Computer Science
Tari Rorohiko

Computing and Mathematical Sciences

Working Papers 2014

Working Paper Series index


01/2014 - FSEA 2014 - Proceedings of the AVI 2014 Workshop on Fostering Smart Energy Applications through Advanced Visual Interfaces
04/2014 - Fully supervised training of Gaussian radial basis function networks in WEKA

FSEA 2014 - Proceedings of the AVI 2014 Workshop on Fostering Smart Energy Applications through Advanced Visual Interfaces PDF

M. Masoodian, E. André, S. Luz and T. Rist
No. 01/2014

It is with great pleasure that we welcome you to FSEA 2014, the AVI 2014 workshop on Fostering Smart Energy Applications through Advanced Visual Interfaces.

This workshop focuses on advanced interaction, interface, and visualization techniques for energy-related applications, tools, and services. It brings together researchers and practitioners from a diverse range of background, including interaction design, human-computer interaction, visualization, computer games, and other fields concerned with the development of advanced visual interfaces for smart energy applications.

FSEA 2014 is the result of the efforts of many people involved in its organization, including our programme committee, and others who have assisted us in putting this workshop together.

Fully supervised training of Gaussian radial basis function networks in WEKA PDF

E. Frank
No. 04/2014

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 [2] 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.

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