Metraipolis › archive › dc_feb79fdbb46e

Meta-learning (computer science) — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Meta-learning_(computer_science)
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-06T00:34:25.689Z
SHA-256 of the archived text: 88bd10980610d7ef6789e20b8a27578f2a82ea624646797226fcde234cc067c8
Stable URL of this page: https://metraipolis.com/documento/dc_feb79fdbb46e

How to cite

Meta-learning (computer science) — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Meta-learning_(computer_science) (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_feb79fdbb46e (Metraipolis archive dc_feb79fdbb46e, sha256 88bd10980610d7ef6789e20b8a27578f2a82ea624646797226fcde234cc067c8)

BibTeX

@misc{dcfeb79fdbb46e,
  title = {Meta-learning (computer science) — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Meta-learning_(computer_science)},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_feb79fdbb46e; sha256 88bd10980610d7ef6789e20b8a27578f2a82ea624646797226fcde234cc067c8},
  year = {2026}
}

Archived text

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

Meta-learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself, hence the alternative term learning to learn. Flexibility is important because each learning algorithm is based on a set of assumptions about the data, its inductive bias. This means that it will only learn well if the bias matches the learning problem. A learning algorithm may perform very well in one domain, but not on the next. This poses strong restrictions on the use of machine learning or data mining techniques, since the relationship between the learning problem (often some kind of database) and the effectiveness of different learning algorithms is not yet understood. By using different kinds of metadata, like properties of the learning problem, algorithm properties (like performance measures), or patterns previously derived from the data, it is possible to learn, select, alter or combine different learning algorithms to effectively solve a given learning problem. Critiques of meta-learning approaches bear a strong resemblance to the critique of metaheuristic, a possibly related problem. A good analogy to meta-learning, and the inspiration for Jürgen Schmidhuber's early work (1987) and Yoshua Bengio et al.'s work (1991), considers that genetic evolution learns the learning procedure encoded in genes and executed in each individual's brain. In an open-ended hierarchical meta-learning system using genetic programming, better evolutionary methods can be learned by meta evolution, which itself can be improved by meta meta evolution, etc.

Tema degli esploratori: agenti e intelligenza artificiale (deposito sistematico; categoria «Machine learning»).
Fonte: Wikipedia in inglese, voce «Meta-learning (computer science)» — https://en.wikipedia.org/wiki/Meta-learning_(computer_science)
Wikidata Q6822311 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q6822311
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Meta-learning_(computer_science)?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 1365457010, consultata il 2026-10-05T23:46:55.433Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: 14af83d954b59019f05da8dcc9131c2da028b2e80fe13d1d6857e1b475595939