Interpolation Networks

dc.contributor.authorNunes, Luís
dc.contributor.authorAlmeida, Luís B.
dc.contributor.authorLanglois, Thibault
dc.date.accessioned2013-07-11T14:30:02Z
dc.date.available2013-07-11T14:30:02Z
dc.date.issued2013-07-11
dc.description.abstractThis paper introduces a new type of network based on local response units, the ‘Intelpolation Networh’ (INS).U nder certain conditions this network is an interpolator. Its formulation allows a type of initialisation by prototypes that will set the net in a good initial starting point for the subsequent supervised learning process. These networks can be seen as a type of ‘Radial Basis Functions Network’ (RBFN) [Moody88]. However, their basis functions are essentially inverse squared distances instead of Gaussian functions. A brief description of the origin and motivation of INS is made in the first section, followed by the description of the first experiments with these networks.por
dc.event.date1996por
dc.event.locationWashington DC, USApor
dc.event.titleInternational Conference on Neural Networks (ICNN96)por
dc.event.typeConferênciapor
dc.identifier.urihttp://hdl.handle.net/10071/5312
dc.language.isoengpor
dc.pagination1750-1754por
dc.peerreviewedSimpor
dc.publicationstatusPublicadopor
dc.rightsrestricted accesspor
dc.subjectInterpolation Networkspor
dc.subjectRadial Basis Functionspor
dc.subjectinitialisation by prototypespor
dc.subjectNeural Networkspor
dc.titleInterpolation Networkspor
dc.typeconferenceObjectpor
dspace.entity.typePublicationen

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