Rvm-based multi-class classification of remotely sensed data

HIGHLIGHTS

  • who: Giles Foody from the School of Geography University of UK have published the research: RVM-based multi-class classification of remotely sensed data, in the Journal: (JOURNAL) of 21/05/2007
  • what: Much of this research has focused on the potential of new classifiers to accurately discriminate between classes.
  • future: There are however extensions of the basic approach that may be used for multi-class classification. The relevance vector machine (RVM) a Bayesian extension of the support vector machine (SVM) has considerable potential for the analysis of remotely sensed data.
 

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