Neural gas

Neural gas is an artificial neural network, inspired by the self-organizing map and introduced in 1991 by Thomas Martinetz and Klaus Schulten.^[1] The neural gas is a simple algorithm for finding optimal data representations based on feature vectors. The algorithm was coined "neural gas" because of the dynamics of the feature vectors during the adaptation process, which distribute themselves like a gas within the data space. It is applied where data compression or vector quantization is an issue, for example speech recognition,^[2] image processing^[3] or pattern recognition. As a robustly converging alternative to the k-means clustering it is also used for cluster analysis.^[4]

^ Thomas Martinetz and Klaus Schulten (1991). "A "neural gas" network learns topologies" (PDF). Artificial Neural Networks. Elsevier. pp. 397–402.
^ F. Curatelli; O. Mayora-Iberra (2000). "Competitive learning methods for efficient Vector Quantizations in a speech recognition environment". In Osvaldo Cairó; L. Enrique Sucar; Francisco J. Cantú-Ortiz (eds.). MICAI 2000: Advances in artificial intelligence : Mexican International Conference on Artificial Intelligence, Acapulco, Mexico, April 2000 : proceedings. Springer. p. 109. ISBN 978-3-540-67354-5.
^ Angelopoulou, Anastassia; Psarrou, Alexandra; Garcia Rodriguez, Jose; Revett, Kenneth (2005). "Computer Vision for Biomedical Image Applications". In Yanxi Liu; Tianzi Jiang; Changshui Zhang (eds.). Computer vision for biomedical image applications: first international workshop, CVBIA 2005, Beijing, China, October 21, 2005 : proceedings. Lecture Notes in Computer Science. Vol. 3765. Springer. p. 210. doi:10.1007/11569541_22. ISBN 978-3-540-29411-5.
^ Fernando Canales; Max Chacon (2007). "Progress in Pattern Recognition, Image Analysis and Applications". In Luis Rueda; Domingo Mery (eds.). Progress in pattern recognition, image analysis and applications: 12th Iberoamerican Congress on Pattern Recognition, CIARP 2007, Viña del Mar-Valparaiso, Chile, November 13–16, 2007; proceedings. Lecture Notes in Computer Science. Vol. 4756. Springer. pp. 684–693. doi:10.1007/978-3-540-76725-1_71. ISBN 978-3-540-76724-4.