| http://dbpedia.org/ontology/abstract
|
Теория вычислительного обучения (англ. computational learning theory, или просто теория обучения) — это подобласть теории искусственного интеллекта, посвящённая разработке и анализу алгоритмов машинного обучения.
, A teoria da aprendizagem computacional é u … A teoria da aprendizagem computacional é um sub-campo da inteligência artificial e teoria da computação que busca construir modelos formais de projeto e analise de agentes de aprendizado baseados em máquinas de estado. Alguns modelos famosos propostos são o Provavelmente Aproximadamente Correto, o AIXI, Máquinas de Gödel, Indução Universal de Solomonoff e Identificação de Linguagem no Limite. Diferente da , que busca projetar e entender ferramentas que extraem padrões estatísticos nos dados pelo método indutivo, a teoria da aprendizagem computacional tem maior foco em entender os limites computacionais de qualquer agente inferencial, como representações simbólico numéricas, complexidade computacional e decidibilidade. Trabalhos mais recentes no campo tem explorado o projeto de agentes que atuam no processo de aprendizado, como ou .tuam no processo de aprendizado, como ou .
, In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms.
, De computationele leertheorie is een onderdeel van de theoretische informatica waarin algoritmes die gebruikt worden in het machinaal leren worden geanalyseerd.
|
| http://dbpedia.org/ontology/wikiPageExternalLink
|
http://research.microsoft.com/adapt/MSBNx/msbnx/Basics_of_Bayesian_Inference.htm +
, http://citeseerx.ist.psu.edu/viewdoc/summary%3Fdoi=10.1.1.47.2224 +
, http://portal.acm.org/citation.cfm%3Fid=129712.129746 +
, http://citeseer.ist.psu.edu/dhagat94pac.html +
, http://www.wisdom.weizmann.ac.il/~oded/PS/ul.ps +
, http://www.montefiore.ulg.ac.be/~geurts/Cours/AML/Readings/Valiant.pdf +
, http://citeseer.ist.psu.edu/schapire90strength.html +
, http://citeseer.ist.psu.edu/haussler90probably.html +
, http://citeseer.ist.psu.edu/kearns89cryptographic.html +
, http://citeseer.ist.psu.edu/kearns93efficient.html +
, http://citeseer.ist.psu.edu/kearns93learning.html +
, http://web.mit.edu/~6.863/www/spring2009/readings/gold67limit.pdf +
, http://www.trhvidsten.com/docs/classics/Blumer-1989.pdf +
, https://courses.engr.illinois.edu/ece544na/fa2014/vapnik71.pdf +
, http://www.cse.buffalo.edu/~hungngo/classes/2008/694/papers/occam.pdf +
|
| http://dbpedia.org/ontology/wikiPageID
|
387537
|
| http://dbpedia.org/ontology/wikiPageInterLanguageLink
|
http://de.dbpedia.org/resource/Maschinelles_Lernen +
|
| http://dbpedia.org/ontology/wikiPageLength
|
8686
|
| http://dbpedia.org/ontology/wikiPageRevisionID
|
1124273696
|
| http://dbpedia.org/ontology/wikiPageWikiLink
|
http://dbpedia.org/resource/Support_vector_machine +
, http://dbpedia.org/resource/VC_dimension +
, http://dbpedia.org/resource/VC_theory +
, http://dbpedia.org/resource/Supervised_learning +
, http://dbpedia.org/resource/Boosting_%28machine_learning%29 +
, http://dbpedia.org/resource/Error_Tolerance_%28PAC_learning%29 +
, http://dbpedia.org/resource/Belief_networks +
, http://dbpedia.org/resource/Category:Computational_learning_theory +
, http://dbpedia.org/resource/Category:Theoretical_computer_science +
, http://dbpedia.org/resource/Grammar_induction +
, http://dbpedia.org/resource/Leslie_Valiant +
, http://dbpedia.org/resource/Vladimir_Vapnik +
, http://dbpedia.org/resource/Computer_science +
, http://dbpedia.org/resource/Inference +
, http://dbpedia.org/resource/E._Mark_Gold +
, http://dbpedia.org/resource/Category:Machine_learning +
, http://dbpedia.org/resource/Polynomial_time +
, http://dbpedia.org/resource/Occam_learning +
, http://dbpedia.org/resource/Manfred_K._Warmuth +
, http://dbpedia.org/resource/Artificial_intelligence +
, http://dbpedia.org/resource/Alexey_Chervonenkis +
, http://dbpedia.org/resource/Information_theory +
, http://dbpedia.org/resource/Dana_Ron +
, http://dbpedia.org/resource/P_versus_NP_problem +
, http://dbpedia.org/resource/One-way_function +
, http://dbpedia.org/resource/Probably_approximately_correct_learning +
, http://dbpedia.org/resource/Frequency_probability +
, http://dbpedia.org/resource/Algorithmic_learning_theory +
, http://dbpedia.org/resource/Probability +
, http://dbpedia.org/resource/Online_machine_learning +
, http://dbpedia.org/resource/Bayesian_probability +
, http://dbpedia.org/resource/List_of_important_publications_in_computer_science +
, http://dbpedia.org/resource/Machine_learning +
, http://dbpedia.org/resource/Oded_Goldreich +
, http://dbpedia.org/resource/Distribution_learning_theory +
, http://dbpedia.org/resource/Dana_Angluin +
, http://dbpedia.org/resource/Bayesian_inference +
, http://dbpedia.org/resource/Boosting_%28meta-algorithm%29 +
, http://dbpedia.org/resource/Cryptography +
, http://dbpedia.org/resource/Stability_%28learning_theory%29 +
, http://dbpedia.org/resource/Category:Computational_fields_of_study +
|
| http://dbpedia.org/property/wikiPageUsesTemplate
|
http://dbpedia.org/resource/Template:Cite_journal +
, http://dbpedia.org/resource/Template:Short_description +
, http://dbpedia.org/resource/Template:Citation_needed +
, http://dbpedia.org/resource/Template:Differentiable_computing +
, http://dbpedia.org/resource/Template:More_citations_needed +
, http://dbpedia.org/resource/Template:Reflist +
, http://dbpedia.org/resource/Template:Machine_learning +
, http://dbpedia.org/resource/Template:Spaced_ndash +
, http://dbpedia.org/resource/Template:See_also +
|
| http://purl.org/dc/terms/subject
|
http://dbpedia.org/resource/Category:Machine_learning +
, http://dbpedia.org/resource/Category:Computational_fields_of_study +
, http://dbpedia.org/resource/Category:Computational_learning_theory +
, http://dbpedia.org/resource/Category:Theoretical_computer_science +
|
| http://purl.org/linguistics/gold/hypernym
|
http://dbpedia.org/resource/Analysis +
|
| http://www.w3.org/ns/prov#wasDerivedFrom
|
http://en.wikipedia.org/wiki/Computational_learning_theory?oldid=1124273696&ns=0 +
|
| http://xmlns.com/foaf/0.1/isPrimaryTopicOf
|
http://en.wikipedia.org/wiki/Computational_learning_theory +
|
| owl:sameAs |
http://ru.dbpedia.org/resource/%D0%A2%D0%B5%D0%BE%D1%80%D0%B8%D1%8F_%D0%B2%D1%8B%D1%87%D0%B8%D1%81%D0%BB%D0%B8%D1%82%D0%B5%D0%BB%D1%8C%D0%BD%D0%BE%D0%B3%D0%BE_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D1%8F +
, http://www.wikidata.org/entity/Q2462783 +
, http://nl.dbpedia.org/resource/Computationele_leertheorie +
, http://yago-knowledge.org/resource/Computational_learning_theory +
, http://pt.dbpedia.org/resource/Teoria_da_aprendizagem_computacional +
, http://rdf.freebase.com/ns/m.022d7w +
, http://fa.dbpedia.org/resource/%D9%86%D8%B8%D8%B1%DB%8C%D9%87_%DB%8C%D8%A7%D8%AF%DA%AF%DB%8C%D8%B1%DB%8C_%D9%85%D8%AD%D8%A7%D8%B3%D8%A8%D8%A7%D8%AA%DB%8C +
, https://global.dbpedia.org/id/2Km6r +
, http://dbpedia.org/resource/Computational_learning_theory +
|
| rdf:type |
http://dbpedia.org/ontology/Work +
|
| rdfs:comment |
De computationele leertheorie is een onderdeel van de theoretische informatica waarin algoritmes die gebruikt worden in het machinaal leren worden geanalyseerd.
, A teoria da aprendizagem computacional é u … A teoria da aprendizagem computacional é um sub-campo da inteligência artificial e teoria da computação que busca construir modelos formais de projeto e analise de agentes de aprendizado baseados em máquinas de estado. Alguns modelos famosos propostos são o Provavelmente Aproximadamente Correto, o AIXI, Máquinas de Gödel, Indução Universal de Solomonoff e Identificação de Linguagem no Limite. Diferente da , que busca projetar e entender ferramentas que extraem padrões estatísticos nos dados pelo método indutivo, a teoria da aprendizagem computacional tem maior foco em entender os limites computacionais de qualquer agente inferencial, como representações simbólico numéricas, complexidade computacional e decidibilidade. Trabalhos mais recentes no campo tem explorado o projeto de agentes que po tem explorado o projeto de agentes que
, In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms.
, Теория вычислительного обучения (англ. computational learning theory, или просто теория обучения) — это подобласть теории искусственного интеллекта, посвящённая разработке и анализу алгоритмов машинного обучения.
|
| rdfs:label |
Computationele leertheorie
, Теория вычислительного обучения
, Computational learning theory
, Teoria da aprendizagem computacional
|
| rdfs:seeAlso |
http://dbpedia.org/resource/Statistical_learning_theory +
|
