Self-organized formation of topographically correct feature maps

By Teuvo Kohonen. 1982

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Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “…& Mitchison, 1989; Barrow, 1987; Deco & Parra, 1997; Field, 1987; Földiák, 1990; Földiák & Young, 1995; Grossberg, 1976a, 1976b; Hebb, 1949; Kohonen, 1972, 1982, 1988; Kosko, 1990; Martinetz, Ritter, & Schulten, 1990; Miller, 1994; Mozer, 1991; Oja, 1989; Palm, 1992; Pearlmutter &Hinton,…”
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “Unsupervised learning is implemented in neural networks with self-Organising Maps (SOM) (Kohonen, 1982, 1990), auto-encoders (Bourlard and Kamp, 1988), restricted Boltzmann machines (RBM) (Hinton and Salakhutdinov, 2006), and Hebbian plasticity (Hebb, 1949; Gerstner and Kistler, 2002a; Cooper,…”
Optimal unsupervised learning in a single-layer linear feedforward neural network (1989, methodology): “This technique is used to ensure that different outputs learn different functions of the input (Barrow, 1987; Grossberg, 1976; Kohonen, 1982, 1988, among others).”
Euclid preparation (2021, methodology): “The target selection is based on an unsupervised machine-learning technique, the self-organising map (SOM, Kohonen 1982), which they use to define a spectroscopic target sample that is representative in terms of the galaxy colours of the Euclid cosmic shear sample.”
Machine Learning and Radiology (2012, methodology): “The selforganizing map (SOM) was introduced around the same time (Kohonen, 1982).”
Essentials of the self-organizing map (2013, background): “…this author introduced a new nonlinearly projecting mapping , called the self-organizing map (SOM) , which otherwise resembles the VQ, but in which, additionally, the models (corresponding to the codebook vectors in the VQ) become spatially, globally ordered (Kohonen, 1982a, 1982b, 1990, 2001).”
Machine learning in geosciences and remote sensing (2016, methodology): “SOMs are a data visualization and unsupervised classi ﬁ cation technique invented by Kohonen (1982).”
Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems (1998, background): “Krose and Eecen (1994), Zimmer (1996) and Nehmzov (1996) showed that for relatively simple workspaces, localization problems for robots can be solved using the topology preserving map scheme (Kohonen, 1982). It is, however, difficult to scale-up this scheme as the very plain representation by a sing”