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A study of parameter values for a Mahalanobis Distance fuzzy classifier
A supervised Mahalanobis Distance fuzzy classifier (and the related fuzzy c-means clustering algorithm) requires the a priori selection of a weighting parameter called the fuzzy exponent. Guidance in the existing literature on an appropriate value is not definitive. This paper attempts to rigorously justify previous experimental findings on suitable values for this fuzzy exponent, using the criterion that fuzzy set memberships reflect class proportions in the mixed pixels of a remotely sensed image.
History
Journal
Fuzzy sets and systemsVolume
137Issue
2Pagination
191 - 213Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0165-0114Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2002, Elsevier Science B.V.Usage metrics
Keywords
Fuzzy classificationImage processingScience & TechnologyTechnologyPhysical SciencesComputer Science, Theory & MethodsMathematics, AppliedStatistics & ProbabilityComputer ScienceMathematicsTHEMATIC MAPPER DATAREMOTELY-SENSED DATALAND-COVERC-MEANSSETSREPRESENTATIONMEMBERSHIPALGORITHMEXAMPLEArtificial Intelligence and Image Processing
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