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Summary: Computing OWA weights as relevance factors
Angel Caaron
Department of Electronics and Computers, TRANSILVANIA University of Braov, Romania
e-mail: cataron@vega.unitbv.ro
Rzvan Andonie
Computer Science Department, Central Washington University, Ellensburg, USA*
e-mail: andonie@cwu.edu
*
On leave of absence from the Department of Electronics and
Computers, Transylvania University of Braov.
Abstract Ordered Weighted Aggregation (OWA) operators
represent a distinct family of aggregation operators and were
introduced by Yager in [1]. They compute a weighted sum of a
number of criteria that must be satisfied. The central element of
the OWA operators is that the criteria are reordered before
aggregation and therefore a particular weight is associated to a
position.
Relevance Learning Vector Quantization (RLVQ) [2] is an
extension of the Learning Vector Quantization (LVQ) algorithm
[3] and performs a heuristic determination of the relevance
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