On the learning of weights in some aggregation operators: the weigthed mean and OWA operators.
Mathware and Soft Computing (1999)
- Volume: 6, Issue: 2-3, page 249-265
- ISSN: 1134-5632
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topTorra, Vicenç. "On the learning of weights in some aggregation operators: the weigthed mean and OWA operators.." Mathware and Soft Computing 6.2-3 (1999): 249-265. <http://eudml.org/doc/39170>.
@article{Torra1999,
abstract = {We study the determination of weights for two types of aggregation operators: the weighted mean and the OWA operator. We assume that there is at our disposal a set of examples for which the outcome of the aggregation operator is known. In the case of the OWA operator, we compare the results obtained by our method with another one in the literature. We show that the optimal weighting vector is reached with less cost.},
author = {Torra, Vicenç},
journal = {Mathware and Soft Computing},
keywords = {Operadores lógicos; Inteligencia artificial; Lógica difusa; Media ponderada; neural networks; hiearchies of quasiarithmetic means},
language = {eng},
number = {2-3},
pages = {249-265},
title = {On the learning of weights in some aggregation operators: the weigthed mean and OWA operators.},
url = {http://eudml.org/doc/39170},
volume = {6},
year = {1999},
}
TY - JOUR
AU - Torra, Vicenç
TI - On the learning of weights in some aggregation operators: the weigthed mean and OWA operators.
JO - Mathware and Soft Computing
PY - 1999
VL - 6
IS - 2-3
SP - 249
EP - 265
AB - We study the determination of weights for two types of aggregation operators: the weighted mean and the OWA operator. We assume that there is at our disposal a set of examples for which the outcome of the aggregation operator is known. In the case of the OWA operator, we compare the results obtained by our method with another one in the literature. We show that the optimal weighting vector is reached with less cost.
LA - eng
KW - Operadores lógicos; Inteligencia artificial; Lógica difusa; Media ponderada; neural networks; hiearchies of quasiarithmetic means
UR - http://eudml.org/doc/39170
ER -
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