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Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps

Martinetz, Thomas·1993·ICANN ’93

DOI 10.1007/978-1-4471-2063-6_104

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The Organization of Behavior
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Ritter H, Martinetz T, Schulten K (1992), Neural Computation and Self-Organizing Maps. Addison-Wesley, Reading, Mass., 1992.
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Liu, H., Kurihara, M., Oyama, S., & Sato, H. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6706725 · Cited by 1

Authors: Martinetz, T.
Affiliations: Siemens (Germany) (DE)
Corresponding author: Martinetz, Thomas
ORCID: Martinetz, Thomas
Venue: ICANN ’93
Publisher: Springer London
Published: 1993
Pages: 427-434
DOI: 10.1007/978-1-4471-2063-6_104
OpenAlex: W2112796159
Cited by: 256
References: 11
Citations by year: 2026: 2 · 2025: 5 · 2024: 2 · 2023: 7 · 2022: 7 · 2021: 8 · 2020: 6 · 2019: 6 · 2018: 9 · 2017: 8 · 2016: 14 · 2015: 10 · 2014: 11 · 2013: 6 · 2012: 10
FWCI: 3.16
Type: conference paper
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Springer eBooks (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
Keywords: Hebbian learning, competitive learning, feature manifold, Delaunay triangulation, lateral connections, neighborhood relations, neural network architecture, manifold learning

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Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps

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