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 makalah ini, kami merawat kekuatan penyesuaian novel mengikut kedudukan kejiranan rangkaian saraf yang mengatur sendiri dengan objektif untuk mengelakkan pergantungan awal vektor rujukan, yang berkaitan dengan kekuatan dalam rangkaian neural-gas yang dicadangkan oleh Martinetz et al. Pendekatan sekarang mempamerkan keberkesanan dalam herotan purata berbanding teknik konvensional melalui eksperimen berangka. Tambahan pula pendekatan sekarang digunakan untuk data imej dan kesahan dalam menggunakan sebagai sistem pengekodan imej diperiksa.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Michiharu MAEDA, Hiromi MIYAJIMA, "Adaptation Strength According to Neighborhood Ranking of Self-Organizing Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 9, pp. 2078-2082, September 2002, doi: .
Abstract: In this paper we treat a novel adaptation strength according to neighborhood ranking of self-organizing neural networks with the objective of avoiding the initial dependency of reference vectors, which is related to the strength in the neural-gas network suggested by Martinetz et al. The present approach exhibits the effectiveness in the average distortion compared to the conventional technique through numerical experiments. Furthermore the present approach is applied to image data and the validity in employing as an image coding system is examined.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_9_2078/_p
Salinan
@ARTICLE{e85-a_9_2078,
author={Michiharu MAEDA, Hiromi MIYAJIMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Adaptation Strength According to Neighborhood Ranking of Self-Organizing Neural Networks},
year={2002},
volume={E85-A},
number={9},
pages={2078-2082},
abstract={In this paper we treat a novel adaptation strength according to neighborhood ranking of self-organizing neural networks with the objective of avoiding the initial dependency of reference vectors, which is related to the strength in the neural-gas network suggested by Martinetz et al. The present approach exhibits the effectiveness in the average distortion compared to the conventional technique through numerical experiments. Furthermore the present approach is applied to image data and the validity in employing as an image coding system is examined.},
keywords={},
doi={},
ISSN={},
month={September},}
Salinan
TY - JOUR
TI - Adaptation Strength According to Neighborhood Ranking of Self-Organizing Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2078
EP - 2082
AU - Michiharu MAEDA
AU - Hiromi MIYAJIMA
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2002
AB - In this paper we treat a novel adaptation strength according to neighborhood ranking of self-organizing neural networks with the objective of avoiding the initial dependency of reference vectors, which is related to the strength in the neural-gas network suggested by Martinetz et al. The present approach exhibits the effectiveness in the average distortion compared to the conventional technique through numerical experiments. Furthermore the present approach is applied to image data and the validity in employing as an image coding system is examined.
ER -