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An incremental self-organizing neural network based on enhanced competitive Hebbian learning

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/65582

Title: An incremental self-organizing neural network based on enhanced competitive Hebbian learning
Authors: Liu, Hao Browse this author
Kurihara, Masahito Browse this author →KAKEN DB
Oyama, Satoshi Browse this author →KAKEN DB
Sato, Haruhiko Browse this author →KAKEN DB
Issue Date: 2013
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Citation: The 2013 International Joint Conference on Neural Networks (IJCNN), ISBN: 978-1-4673-6129-3
Start Page: 1
End Page: 8
Publisher DOI: 10.1109/IJCNN.2013.6706725
Abstract: Self-organizing neural networks are important tools for realizing unsupervised learning. Recently, a difficult task has involved the incremental, efficient and robust learning in noisy environments. Most of the existing techniques are poor in this regard. In this paper, we first propose a new topology generating method called enhanced competitive Hebbian learning (enhanced CHL), and then propose a novel incremental self-organizing neural network based on the enhanced CHL method, called enhanced incremental growing neural gas (Hi- GNG). The experiments presented in this paper show that the Hi-GNG algorithm can automatically and efficiently generate a topological structure with a suitable number of neurons and that the proposed algorithm is robust to noisy data.
Conference Name: IEEE International Joint Conference on Neural Networks (IJCNN)
Conference Sequence: 2013
Conference Place: Dallas, Texas
Rights: © 2013 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/2115/65582
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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