Content deleted Content added
m Comp.arch moved page Meta learning (computer science) to Meta-learning (computer science) over redirect |
No edit summary |
||
Line 14:
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" />
See also [[Ensemble learning]].
== Definition ==
| |||