About: Meta-learning (computer science)

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Meta learningis a subfield of machine learning where automatic learning algorithms are applied to 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.

Attributes	Values
rdfs:label	Metaaprendizaje (ciencias de la computación) (es) Meta-learning (computer science) (en)
rdfs:comment	El metaaprendizaje es un subcampo del aprendizaje automático (AU). La idea es aplicar algoritmos de AU a los metadatos de otros algoritmos de AU, de ahí la denominación alternativa: aprender a aprender. El objetivo es entender como resolver problemas de aprendizaje con mayor flexibilidad, mejorando el desempeño de algoritmos existentes o induciendo al algoritmo de aprendizaje en si. Se utilizan diferentes tipos de metadatos como las propiedades del problema de aprendizaje, las del propio algoritmo (como mediciones de desempeño) o patrones derivados directamente de la los datos. (es) Meta learningis a subfield of machine learning where automatic learning algorithms are applied to 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. (en)
dcterms:subject	Machine learning
Wikipage page ID	4615464 (xsd:integer)
Wikipage revision ID	1119676193 (xsd:integer)
Link from a Wikipage to another Wikipage	Case-based reasoning Scenario Scholarpedia Neural network Meta-knowledge Metaheuristic Inductive bias Chelsea Finn Genetic programming Optimization algorithm Constraint satisfaction Meta-optimization Recurrent neural networks Reinforcement Learning Machine learning Bias-variance dilemma Automated theorem proving Backpropagation Machine learning Data mining Gödel machine Jürgen Schmidhuber Learning classifier system Inductive transfer Database K-nearest neighbor algorithm Classification rule Applied Behavioral Analysis Meta-data Metric space Neural Network Recursive self-improvement Sepp Hochreiter Yoshua Bengio Long short-term memory ImageNet AutoML Boosting (meta-algorithm) Learning algorithms Learning to learn Deepmind
Link from a Wikipage to an external page	http://axon.cs.byu.edu/Dan/478/misc/Vilalta.pdf https://books.google.com/books%3Fid=-Gsi_cxZGpcC&printsec=frontcover%23v=onepage&q&f=false https://www.youtube.com/watch%3Fv=IkDw22a8BDE https://www.youtube.com/watch%3Fv=j8qDaVfrO_c https://www.youtube.com/watch%3Fv=rHGPfl0pvLY http://www.scholarpedia.org/article/Metalearning
sameAs	Meta-learning (computer science) Meta-learning (computer science) Meta-learning (computer science) Meta-learning (computer science)
dbp:wikiPageUsesTemplate	dbt:About dbt:More_citations_needed dbt:Reflist dbt:Short_description
has abstract	El metaaprendizaje es un subcampo del aprendizaje automático (AU). La idea es aplicar algoritmos de AU a los metadatos de otros algoritmos de AU, de ahí la denominación alternativa: aprender a aprender. El objetivo es entender como resolver problemas de aprendizaje con mayor flexibilidad, mejorando el desempeño de algoritmos existentes o induciendo al algoritmo de aprendizaje en si. La flexibilidad es importante porque cada algoritmo de AU está basado en una serie de asunciones sobre los datos, su sesgo inductivo. Esto quiere decir que el algoritmo solo aprenderá correctamente si su sesgo inductivo se ajusta al problema de aprendizaje (usualmente una base de datos). Por ende, un algoritmo de AU se desempeña bien en un dominio específico pero no en otros. Este fenómeno imprime fuertes restricciones al uso del aprendizaje automático o minería de datos, dado que la relación entre el problema de aprendizaje y la efectividad de diferentes algoritmos de AU aún no se entiende bien. Se utilizan diferentes tipos de metadatos como las propiedades del problema de aprendizaje, las del propio algoritmo (como mediciones de desempeño) o patrones derivados directamente de la los datos. De esta manera es posible aprender, seleccionar, alterar o combinar diferentes algoritmos de AU para buscar los resultados que mejor satisfagan el problema de aprendizaje. Una buena analogía es que la evolución genética aprende del procedimiento de aprendizaje codificado en los genes y ejecutado en la cerebro de cada individuo como plantea Bengio et al.'s en su trabajo temprano (1991). (es) Meta learningis a subfield of machine learning where automatic learning algorithms are applied to 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. Flexibility is important because each learning algorithm is based on a set of assumptions about the data, its inductive bias. 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) and Yoshua Bengio et al.'s work (1991), 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 using genetic programming, better evolutionary methods can be learned by meta evolution, which itself can be improved by meta meta evolution, etc. (en)
prov:wasDerivedFrom	wikipedia-en:Meta-learning_(computer_science)?oldid=1119676193&ns=0
page length (characters) of wiki page	20022 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Meta-learning_(computer_science)
is Link from a Wikipage to another Wikipage of	Algorithm selection Weighted majority algorithm (machine learning) Genetic programming Neural architecture search Machine learning Shlomo Argamon Complexity Gödel machine Jürgen Schmidhuber Automated machine learning Meta learning (computer science) OpenAI Sepp Hochreiter Long short-term memory Unsupervised learning Outline of machine learning Paraphrasing (computational linguistics) Meta learning (Computer Science)
is Wikipage redirect of	Meta learning (computer science) Meta learning (Computer Science)
is known for of	Jürgen Schmidhuber
is foaf:primaryTopic of	wikipedia-en:Meta-learning_(computer_science)

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