Content deleted Content added
Changed i.e. to e.g. - preferring smaller hypotheses is an example heuristic, not the essence of procedural bias - also added citation |
Citation bot (talk | contribs) Add: pages, volume, bibcode. Formatted dashes. | You can use this bot yourself. Report bugs here. | Activated by User:Zppix | Category:Machine learning | via #UCB_Category |
||
Line 1:
{{About|meta learning in machine learning|meta learning in social psychology|Meta learning|metalearning in neuroscience|Metalearning (neuroscience)}}
{{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 }}</ref>
is a subfield of [[machine learning]] where automatic learning algorithms are applied on [[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" />
Line 23:
''Bias'' refers to the assumptions that influence the choice of explanatory hypotheses<ref>{{Cite book|title=Metalearning - Springer|doi=10.1007/978-3-540-73263-1|series = Cognitive Technologies|year = 2009|isbn = 978-3-540-73262-4|last1 = Brazdil|first1 = Pavel|last2=Carrier|first2=Christophe Giraud|last3=Soares|first3=Carlos|last4=Vilalta|first4=Ricardo}}</ref> and not the notion of bias represented in the [[bias-variance dilemma]]. Meta learning is concerned with two aspects of learning bias.
* Declarative bias specifies the representation of the space of hypotheses, and affects the size of the search space (e.g., represent hypotheses using linear functions only).
* Procedural bias imposes constraints on the ordering of the inductive hypotheses (e.g., preferring smaller hypotheses). <ref>{{cite journal |last1=Gordon |first1=Diana |last2=Desjardins |first2=Marie |title=Evaluation and Selection of Biases in Machine Learning |journal=Machine Learning |date=1995 |volume=20 |pages=5–22 |doi=10.1023/A:1022630017346 |url=https://link.springer.com/content/pdf/10.1023/A:1022630017346.pdf |accessdate=27 March 2020}}</ref>
==Common approaches==
| |||