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''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 |access-date=27 March 2020|doi-access=free }}</ref>
==Common approaches==
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