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dc.contributor.authorIsrael, Stevenen_NZ
dc.contributor.authorKasabov, Nikolaen_NZ
dc.date.available2011-04-07T03:05:05Z
dc.date.copyright1996-06en_NZ
dc.identifier.citationIsrael, S., & Kasabov, N. (1996). Improved learning strategies for multimodular fuzzy neural network systems: a case study on image classification (Information Science Discussion Papers Series No. 96/09). University of Otago. Retrieved from http://hdl.handle.net/10523/827en
dc.identifier.urihttp://hdl.handle.net/10523/827
dc.description.abstractThis paper explores two different methods for improved learning in multimodular fuzzy neural network systems for classification. It demonstrates these methods on a case study of satellite image classification using 3 spectral inputs and 10 coastal vegetation covertype outputs. The classification system is a multimodular one; it has one fuzzy neural network per output. All the fuzzy neural networks are trained in parallel for a small number of iterations. Then, the system performance is tested on new data to determine the types of interclass confusion. Two strategies are developed to improve classification performance. First, the individual modules are additionally trained for a very small number of iterations on a subset of the data to decrease the false positive and the false negative errors. The second strategy is to create new units, ‘experts’, which are individually trained to discriminate only the ambiguous classes. So, if the main system classifies a new input into one of the ambiguous classes, then the new input is passed to the ‘experts’ for final classification. Two learning techniques are presented and applied to both classification performance enhancement strategies; the first one reduces omission, or false negative, error; the second reduces comission, or false positive, error. Considerable improvement is achieved by using these learning techniques and thus, making it feasible to incorporate them into a real adaptive system that improves during operation.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleImproved learning strategies for multimodular fuzzy neural network systems: a case study on image classificationen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages9en_NZ
otago.date.accession2011-01-23 20:17:22en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints1063en_NZ
otago.school.eprintsKnowledge Engineering Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
otago.school.eprintsSurveyingen_NZ
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otago.relation.number96/09en_NZ
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