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Learnability theory
Author(s) -
Fulop Sean A.,
Chater Nick
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.526
H-Index - 49
eISSN - 1939-5086
pISSN - 1939-5078
DOI - 10.1002/wcs.1228
Subject(s) - learnability , computer science , artificial intelligence , identification (biology) , variety (cybernetics) , limit (mathematics) , algorithmic learning theory , machine learning , cognitive science , active learning (machine learning) , psychology , mathematics , mathematical analysis , botany , biology
Learnability theory is a body of mathematical and computational results concerning questions such as: when is learning possible? What prior information is required to support learning? What computational or other resources are required for learning to be possible? It is therefore complementary both to the computational project of building machine learning systems and to the scientific project of understanding learning in people and animals through observation and experiment. Learnability theory includes work within a variety of theoretical frameworks, including, for example, identification in the limit, and Bayesian learning, which idealize learning in different ways. Learnability theory addresses one of the foundational questions in cognitive science: to what extent can knowledge be derived from experience? WIREs Cogn Sci 2013, 4:299–306. doi: 10.1002/wcs.1228 This article is categorized under: Linguistics > Language Acquisition