The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, propomos um novo algoritmo de aprendizagem competitiva para treinar redes neurais de camada única para agrupar dados. O algoritmo proposto adota uma nova medida baseada na ideia de “simetria” para que os neurônios compitam entre si com base na distância simétrica em vez da distância euclidiana. Os clusters detectados podem ser um conjunto de clusters de diferentes estruturas geométricas. Quatro conjuntos de dados são testados para ilustrar a eficácia do nosso algoritmo proposto.
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Mu-Chun SU, Chien-Hsing CHOU, "A Competitive Learning Algorithm Using Symmetry" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 4, pp. 680-687, April 1999, doi: .
Abstract: In this paper, we propose a new competitive learning algorithm for training single-layer neural networks to cluster data. The proposed algorithm adopts a new measure based on the idea of "symmetry" so that neurons compete with each other based on the symmetrical distance instead of the Euclidean distance. The detected clusters may be a set of clusters of different geometrical structures. Four data sets are tested to illustrate the effectiveness of our proposed algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_4_680/_p
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@ARTICLE{e82-a_4_680,
author={Mu-Chun SU, Chien-Hsing CHOU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Competitive Learning Algorithm Using Symmetry},
year={1999},
volume={E82-A},
number={4},
pages={680-687},
abstract={In this paper, we propose a new competitive learning algorithm for training single-layer neural networks to cluster data. The proposed algorithm adopts a new measure based on the idea of "symmetry" so that neurons compete with each other based on the symmetrical distance instead of the Euclidean distance. The detected clusters may be a set of clusters of different geometrical structures. Four data sets are tested to illustrate the effectiveness of our proposed algorithm.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - A Competitive Learning Algorithm Using Symmetry
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 680
EP - 687
AU - Mu-Chun SU
AU - Chien-Hsing CHOU
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 1999
AB - In this paper, we propose a new competitive learning algorithm for training single-layer neural networks to cluster data. The proposed algorithm adopts a new measure based on the idea of "symmetry" so that neurons compete with each other based on the symmetrical distance instead of the Euclidean distance. The detected clusters may be a set of clusters of different geometrical structures. Four data sets are tested to illustrate the effectiveness of our proposed algorithm.
ER -