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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
현실 세계에서는 이웃 주택이 물리적으로 인접하거나 서로 가깝다는 것이 항상 사실인 것은 아닙니다. 즉, "이웃"이 항상 "진정한 이웃"은 아닙니다. 본 연구에서는 새로운 SOM(Self-Organizing Map) 알고리즘인 뉴런 간 False-Neighbor 정도를 갖는 SOM(FN-SOM)을 제안합니다. 다양한 입력 데이터에 대한 학습을 통해 FN-SOM의 동작을 조사합니다. FN-SOM은 기존 SOM과 Growing Grid보다 입력 데이터의 분포 상태를 반영하는 보다 효과적인 맵을 얻을 수 있음을 확인합니다.
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부
Haruna MATSUSHITA, Yoshifumi NISHIO, "Self-Organizing Map with False-Neighbor Degree between Neurons for Effective Self-Organization" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 6, pp. 1463-1469, June 2008, doi: 10.1093/ietfec/e91-a.6.1463.
Abstract: In the real world, it is not always true that neighboring houses are physically adjacent or close to each other. in other words, "neighbors" are not always "true neighbors." In this study, we propose a new Self-Organizing Map (SOM) algorithm, SOM with False-Neighbor degree between neurons (called FN-SOM). The behavior of FN-SOM is investigated with learning for various input data. We confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional SOM and Growing Grid.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.6.1463/_p
부
@ARTICLE{e91-a_6_1463,
author={Haruna MATSUSHITA, Yoshifumi NISHIO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Self-Organizing Map with False-Neighbor Degree between Neurons for Effective Self-Organization},
year={2008},
volume={E91-A},
number={6},
pages={1463-1469},
abstract={In the real world, it is not always true that neighboring houses are physically adjacent or close to each other. in other words, "neighbors" are not always "true neighbors." In this study, we propose a new Self-Organizing Map (SOM) algorithm, SOM with False-Neighbor degree between neurons (called FN-SOM). The behavior of FN-SOM is investigated with learning for various input data. We confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional SOM and Growing Grid.},
keywords={},
doi={10.1093/ietfec/e91-a.6.1463},
ISSN={1745-1337},
month={June},}
부
TY - JOUR
TI - Self-Organizing Map with False-Neighbor Degree between Neurons for Effective Self-Organization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1463
EP - 1469
AU - Haruna MATSUSHITA
AU - Yoshifumi NISHIO
PY - 2008
DO - 10.1093/ietfec/e91-a.6.1463
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2008
AB - In the real world, it is not always true that neighboring houses are physically adjacent or close to each other. in other words, "neighbors" are not always "true neighbors." In this study, we propose a new Self-Organizing Map (SOM) algorithm, SOM with False-Neighbor degree between neurons (called FN-SOM). The behavior of FN-SOM is investigated with learning for various input data. We confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional SOM and Growing Grid.
ER -