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Learning stable weights for data of varying dimension

Beliakov, G., James, S., Gomez, D., Rodriguez, J. T. and Montero, J. 2015, Learning stable weights for data of varying dimension, in AGOP 2015: Proceeedings of the Aggregation Operators 2015 International Summer School, [University of Silesia], [Katowice, Poland], pp. 49-54.

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Title Learning stable weights for data of varying dimension
Author(s) Beliakov, G.
James, S.
Gomez, D.
Rodriguez, J. T.
Montero, J.
Conference name Aggregation Operators. International Summer School (8th : 2015 : Katowice, Poland)
Conference location Katowice, Poland
Conference dates 7-10 Jul. 2015
Title of proceedings AGOP 2015: Proceeedings of the Aggregation Operators 2015 International Summer School
Editor(s) Baczyński, Michal
De Baets, Bernard
Mesiar, Radko
Publication date 2015
Conference series Aggregation Operators International Summer School
Start page 49
End page 54
Total pages 6
Publisher [University of Silesia]
Place of publication [Katowice, Poland]
Keyword(s) aggregation functions
R-stability
linear programming
weights learning
Summary In this paper we develop a data-driven weight learning method for weighted quasi-arithmetic means where the observed data may vary in dimension.
ISBN 9788380125193
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, [University of Silesia]
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084918

Document type: Conference Paper
Collection: School of Information Technology
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