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DHARMA - Discriminant hyperplane abstracting residuals minimization algorithm for separating clusters with fuzzy boundariesLearning of discriminant hyperplanes in imperfectly supervised or unsupervised training sample sets with unreliably labeled samples along the fuzzy joint boundaries between sample clusters is discussed, with the discriminant hyperplane designed to be a least-squares fit to the unreliably labeled data points. (Samples along the fuzzy boundary jump back and forth from one cluster to the other in recursive cluster stabilization and are considered unreliably labeled.) Minimization of the distances of these unreliably labeled samples from the hyperplanes does not sacrifice the ability to discriminate between classes represented by reliably labeled subsets of samples. An equivalent unconstrained linear inequality problem is formulated and algorithms for its solution are indicated. Landsat earth sensing data were used in confirming the validity and computational feasibility of the approach, which should be useful in deriving discriminant hyperplanes separating clusters with fuzzy boundaries, given supervised training sample sets with unreliably labeled boundary samples.
Document ID
19760049314
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Dasarathy, B. V.
(Computer Sciences Corp. Huntsville, Ala., United States)
Date Acquired
August 8, 2013
Publication Date
May 1, 1976
Publication Information
Publication: IEEE
Subject Category
Computer Programming And Software
Accession Number
76A32280
Funding Number(s)
CONTRACT_GRANT: NAS8-21805
Distribution Limits
Public
Copyright
Other

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