Selecting Parameter Values for Mahalanobis Distance Fuzzy Classifiers
File version
Author(s)
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Liu
Date
Size
444650 bytes
4 bytes
File type(s)
application/pdf
text/plain
Location
Melbourne, Australia
License
Abstract
The fuzzy c-means clustering algorithm, and a related supervised classifier, require the a priori selection of a weighting parameter called the fuzzy exponent. This paper investigates suitable values of this fuzzy exponent using the criterion that fuzzy set memberships reflect class proportions in the mixed pixels of a remotely sensed image
Journal Title
Conference Title
Proceedings of the 10th IEEE International Conference on Fuzzy Systems
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
© 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.