A Topology Preserving Neural Network for Nonstationary Distributions
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概要
- 論文の詳細を見る
We propose a learning algorithm for self-organizing neural networks to form a topology preserving map from an input manifold whose topology may dynamically change. Experimental results show that the network using the proposed algorithm can rapidly adjust itself to represent the topology of nonstationary input distributions.
- 社団法人電子情報通信学会の論文
- 1999-07-25
著者
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Takizawa Hiroyuki
Integrated Information Processing Center Niigata University
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NAKAJIMA Taira
Department of Computer and Mathematical Sciences, Graduate School of Information Sciences, Tohoku Un
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KOBAYASHI Hiroaki
Information Synergy Center, Tohoku University
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NAKAMURA Tadao
Department of Computer and Mathematical Sciences, Graduate School of Information Sciences, Tohoku Un
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TAKIZAWA Hiroyuki
Department of Computer and Mathematical Sciences, Graduate School of Information Sciences, Tohoku Un
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KOBAYASHI Hiroaki
Department of Computer and Mathematical Sciences, Graduate School of Information Sciences, Tohoku Un
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Nakajima T
Laboratory Of Enzymology Graduate School Of Agricultural Science Tohoku University
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Kobayashi H
Tokai Univ. Hiratsuka
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Kobayashi H
Information Synergy Center Tohoku University
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Nakamura T
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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Takizawa H
Department Of Applied Chemistry Faculty Of Engineering Kogakuin University
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Nakamura Tadao
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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Kobayashi Hiroaki
Department Of Applied Chemistry School Of Engineering The University Of Tokyo
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Takizawa Hiroyuki
Department of Applied Chemistry, Faculty of Engineering, Kogakuin University
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