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A robust energy artificial neuron based incremental self-organizing neural network with a dynamic structure

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/65595

Title: A robust energy artificial neuron based incremental self-organizing neural network with a dynamic structure
Authors: Liu, Hao Browse this author
Sato, Haruhiko Browse this author →KAKEN DB
Oyama, Satoshi Browse this author →KAKEN DB
Kurihara, Masahito Browse this author →KAKEN DB
Keywords: self-organizing
unsupervised learning
neural network
incremental learning
SOM
REISOD
Issue Date: 2012
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Citation: Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), ISBN: 978-1-4673-1714-6
Start Page: 1806
End Page: 1811
Publisher DOI: 10.1109/ICSMC.2012.6378000
Abstract: Self-organizing neural network which is an unsupervised learning algorithm is to discover the inherent relationships of data. Such technique has become an important tool for data mining, machine learning and pattern recognition. Most self-organizing neural networks have a difficulty in reflecting data distributions precisely if data distributions are very complex. And meanwhile, it is also hard for these algorithms to learn new data incrementally without destroying the previous learnt data. In this paper, we propose a robust energy artificial neuron based incremental self-organizing neural network with a dynamic structure (REISOD). It can adjust the scale of network automatically to adapt the scale of the data set and learn new data incrementally with preserving the former learnt results. Moreover, several experiments show that our algorithm can reflect data distributions precisely.
Conference Name: IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Conference Sequence: 2012
Conference Place: Seoul
Rights: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: proceedings (author version)
URI: http://hdl.handle.net/2115/65595
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山 聡

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