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dc.contributor.authorFrank, Eibe
dc.date.accessioned2014-07-17T05:12:25Z
dc.date.available2014-07
dc.date.available2014-07-17T05:12:25Z
dc.date.issued2014
dc.identifier04/2014
dc.identifier.citationFrank, 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.en
dc.identifier.issn1177-777X
dc.identifier.urihttps://hdl.handle.net/10289/8683
dc.description.abstractRadial 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.
dc.format.extent1 - 5
dc.format.mimetypeapplication/pdf
dc.publisherDepartment of Computer Science, The University of Waikato
dc.relation.ispartofseriesComputer Science Working Papers
dc.titleFully supervised training of Gaussian radial basis function networks in WEKA
dc.typeReport
uow.relation.series04/2014en_NZ
dc.relation.isPartOfWorking Paper Seriesen_NZ
pubs.begin-page1en_NZ
pubs.confidentialfalse
pubs.elements-id82375
pubs.end-page5en_NZ
pubs.organisational-group/Waikato
pubs.organisational-group/Waikato/FCMS
pubs.organisational-group/Waikato/FCMS/Computer Science
pubs.place-of-publicationHamilton, NZ


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