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Comparison of two different PNN training approaches for satellite cloud data classification

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dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorTian, Bin, author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:48:23Z
dc.date.available2007-01-03T04:48:23Z
dc.date.issued2001
dc.description.abstractThis paper presents a training algorithm for probabilistic neural networks (PNNs) using the minimum classification error (MCE) criterion. A comparison is made between the MCE training scheme and the widely used maximum likelihood (ML) learning on a cloud classification problem using satellite imagery data.
dc.description.sponsorshipThis work was supported by the DoD Center for Geosciences/Atmospheric Research (CG/AR) under Contract DAAL01-98-2-0078.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationTian, Bin and Mahmood R. Azimi-Sadjadi, Comparison of Two Different PNN Training Approaches for Satellite Cloud Data Classification, IEEE Transactions on Neural Networks 12, no. 1 (January 2001): 164-168.
dc.identifier.urihttp://hdl.handle.net/10217/926
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©2001 IEEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectprobabilistic neural network
dc.subjectminimum classification error
dc.subjectmaximum likelihood
dc.subjectcloud classification
dc.titleComparison of two different PNN training approaches for satellite cloud data classification
dc.typeText

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