Meta-learning (computer science): Difference between revisions

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}}</ref> 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)<ref name="sch1987" /> and [[Yoshua Bengio]] et al.'s work (1991),<ref>{{cite conference|last1=Bengio|first1=Yoshua|last2=Bengio|first2=Samy|last3=Cloutier|first3=Jocelyn|conference=IJCNN'91|url=http://bengio.abracadoudou.com/publications/pdf/bengio_1991_ijcnn.pdf|date=1991|title=Learning to learn a synaptic rule}}</ref> 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<ref name="sch1987" /> using [[genetic programming]], better evolutionary methods can be learned by meta evolution, which itself can be improved by meta meta evolution, etc.<ref name="sch1987" />. Meta-learning has the potential to significantly improve the efficiency and effectiveness of [https://wisetechcat.com/data-science-and-machine-learning/ machine learning], making it an active area of research.

See also [[Ensemble learning]].