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Hybrid Kohonen self-organizing map — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Hybrid_Kohonen_self-organizing_map
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-07T20:11:12.905Z
SHA-256 of the archived text: 0c383aaf6b48ba373925c6be60eaa1aafecc2a6144536d9d6b01b110a69c10bf
Stable URL of this page: https://metraipolis.com/documento/dc_e99e1c8a605d

How to cite

Hybrid Kohonen self-organizing map — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Hybrid_Kohonen_self-organizing_map (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_e99e1c8a605d (Metraipolis archive dc_e99e1c8a605d, sha256 0c383aaf6b48ba373925c6be60eaa1aafecc2a6144536d9d6b01b110a69c10bf)

BibTeX

@misc{dce99e1c8a605d,
  title = {Hybrid Kohonen self-organizing map — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Hybrid_Kohonen_self-organizing_map},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_e99e1c8a605d; sha256 0c383aaf6b48ba373925c6be60eaa1aafecc2a6144536d9d6b01b110a69c10bf},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/Hybrid_Kohonen_self-organizing_map. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In artificial neural networks, a hybrid Kohonen self-organizing map is a type of self-organizing map (SOM) named for the Finnish professor Teuvo Kohonen, where the network architecture consists of an input layer fully connected to a 2–D SOM or Kohonen layer. The output from the Kohonen layer, which is the winning neuron, feeds into a hidden layer and finally into an output layer. In other words, the Kohonen SOM is the front–end, while the hidden and output layer of a multilayer perceptron is the back–end of the hybrid Kohonen SOM. The hybrid Kohonen SOM was first applied to machine vision systems for image classification and recognition. Hybrid Kohonen SOM has been used in weather prediction and especially in forecasting stock prices, which has made a challenging task considerably easier. It is fast and efficient with less classification error, hence is a better predictor, when compared to Kohonen SOM and backpropagation networks.

Tema degli esploratori: statistica e scienza dei dati (deposito sistematico; ricerca «artificial neural network»).
Fonte: Wikipedia in inglese, voce «Hybrid Kohonen self-organizing map» — https://en.wikipedia.org/wiki/Hybrid_Kohonen_self-organizing_map
Wikidata Q5953103 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q5953103
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Hybrid_Kohonen_self-organizing_map?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1297516371, consultata il 2026-10-07T19:25:58.276Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: 5681229ebd9adc11c75fb030d1e315fcc4928825ae8f9eacd4fc65447e58ab2d