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
Dalam dunia nyata, tidak selalu benar bahawa rumah jiran secara fizikalnya bersebelahan atau berdekatan antara satu sama lain. dengan kata lain, "jiran" tidak selalunya "jiran sejati." Dalam kajian ini, kami mencadangkan algoritma Peta Penyusunan Sendiri (SOM) baharu, SOM dengan ijazah Jiran Palsu antara neuron (dipanggil FN-SOM). Tingkah laku FN-SOM disiasat dengan pembelajaran untuk pelbagai data input. Kami mengesahkan bahawa FN-SOM boleh mendapatkan peta yang lebih berkesan yang mencerminkan keadaan pengedaran data input daripada SOM konvensional dan Grid Berkembang.
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Salinan
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
Salinan
@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},}
Salinan
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 -