Meta-learning (computer science): Difference between revisions

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{{More citations needed|date=August 2010}}

'''Meta learning'''<ref name="sch1987">{{cite journal | last1 = Schmidhuber | first1 = Jürgen | year = 1987| title = Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook | url= http://people.idsia.ch/~juergen/diploma1987ocr.pdf | journal = Diploma Thesis, Tech. Univ. Munich}}</ref><ref name="scholarpedia">{{cite journal | last1 = Schaul | first1 = Tom | last2 = Schmidhuber | first2 = Jürgen | year = 2010| title = Metalearning | url= | journal = Scholarpedia | volume = 5 | issue = 6| page = 4650 | doi=10.4249/scholarpedia.4650| bibcode = 2010SchpJ...5.4650S | doi-access = free }}</ref>

is a subfield of [[machine learning]] where automatic learning algorithms are applied onto [[meta-data|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'''.<ref name="sch1987" />

Flexibility is important because each learning algorithm is based on a set of assumptions about the data, its [[inductive bias]].<ref name="utgoff1986">{{Cite journal