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Learning rule — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Learning_rule
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-07T16:52:27.404Z
SHA-256 of the archived text: 6498a877f30e9e33e8db804dc62e5b787fa6607dae91dcb5ab3571d094486a7d
Stable URL of this page: https://metraipolis.com/documento/dc_1928ca4e4239

How to cite

Learning rule — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Learning_rule (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_1928ca4e4239 (Metraipolis archive dc_1928ca4e4239, sha256 6498a877f30e9e33e8db804dc62e5b787fa6607dae91dcb5ab3571d094486a7d)

BibTeX

@misc{dc1928ca4e4239,
  title = {Learning rule — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Learning_rule},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_1928ca4e4239; sha256 6498a877f30e9e33e8db804dc62e5b787fa6607dae91dcb5ab3571d094486a7d},
  year = {2026}
}

Archived text

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

An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network. It is done by updating the weight and bias levels of a network when it is simulated in a specific data environment. A learning rule may accept existing conditions (weights and biases) of the network, and will compare the expected result and actual result of the network to give new and improved values for the weights and biases. Depending on the complexity of the model being simulated, the learning rule of the network can be as simple as an XOR gate or mean squared error, or as complex as the result of a system of differential equations. The learning rule is one of the factors which decides how fast or how accurately the neural network can be developed. Depending on the process to develop the network, there are three main paradigms of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Tema degli esploratori: statistica e scienza dei dati (deposito sistematico; ricerca «artificial neural network»).
Fonte: Wikipedia in inglese, voce «Learning rule» — https://en.wikipedia.org/wiki/Learning_rule
Wikidata Q6510194 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q6510194
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Learning_rule?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 1311317682, consultata il 2026-10-07T16:20:39.007Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: c8ecf523b327ca338185f39873b93123fc997b2eeca22e712f4e6d86c0d5726c