This HTML5 document contains 97 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
dbohttp://dbpedia.org/ontology/
foafhttp://xmlns.com/foaf/0.1/
n12http://lt.dbpedia.org/resource/
n9http://www.public.asu.edu/~jye02/Software/MALSAR/
n16https://global.dbpedia.org/id/
n4https://web.archive.org/web/20041118134329/http:/big.cs.uiuc.edu/webpage/cumulativeLearning/
n13http://www.cse.wustl.edu/~kilian/research/multitasklearning/
dbthttp://dbpedia.org/resource/Template:
n18https://web.archive.org/web/20131224113826/http:/klcl.pku.edu.cn/member/sunxu/
rdfshttp://www.w3.org/2000/01/rdf-schema#
freebasehttp://rdf.freebase.com/ns/
dbpedia-fahttp://fa.dbpedia.org/resource/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
owlhttp://www.w3.org/2002/07/owl#
wikipedia-enhttp://en.wikipedia.org/wiki/
dbphttp://dbpedia.org/property/
provhttp://www.w3.org/ns/prov#
dbchttp://dbpedia.org/resource/Category:
xsdhhttp://www.w3.org/2001/XMLSchema#
goldhttp://purl.org/linguistics/gold/
wikidatahttp://www.wikidata.org/entity/
dbrhttp://dbpedia.org/resource/

Statements

Subject Item
dbr:Bayesian_interpretation_of_kernel_regularization
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:Reproducing_kernel_Hilbert_space
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:Deep_learning
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:General_game_playing
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:Multi-task_learning
rdf:type
dbo:ProgrammingLanguage
rdfs:label
Multi-task learning
rdfs:comment
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately. Early versions of MTL were called "hints". In a widely cited 1997 paper, Rich Caruana gave the following characterization:
dcterms:subject
dbc:Machine_learning
dbo:wikiPageID
938663
dbo:wikiPageRevisionID
1104310342
dbo:wikiPageWikiLink
dbr:Laplacian_matrix dbr:.NET_Framework dbr:Generalization_error dbr:Decision_trees dbr:Multitask_optimization dbr:Robot_learning dbr:Artificial_intelligence dbr:Sparse_array dbr:Vector-valued_function dbr:Artificial_neural_network dbr:Inductive_transfer dbr:Adjacency_matrix dbr:Machine_learning dbr:Complete_metric_space dbr:Evolutionary_computation dbr:Spam_filtering dbr:Orthogonal dbr:Inductive_bias dbr:Feature_space dbr:Stochastic_gradient_descent dbr:GoogLeNet dbr:Automated_machine_learning dbr:Linear_combination dbr:Regularization_(mathematics) dbr:Multi-label_classification dbr:Classifier_(mathematics) dbr:Human-based_genetic_algorithm dbr:Transfer_learning dbr:Convolutional_neural_network dbr:Multiclass_classification dbr:Representation_learning dbc:Machine_learning dbr:Conditional_random_field dbr:Reproducing_kernel_Hilbert_space dbr:Kernel_methods_for_vector_output dbr:Financial_modeling dbr:C_Sharp_(programming_language) dbr:Coercive_function dbr:Overfitting dbr:Convex_optimization dbr:Inner_product_space dbr:General_game_playing dbr:Loss_function dbr:Regularization_by_spectral_filtering
dbo:wikiPageExternalLink
n4:cumulativeLearning.html n9:index.html n13:multitasklearning.html n18:code.htm
owl:sameAs
n12:Daugialypis_mokymasis n16:4sEXm dbpedia-fa:یادگیری_چند-وظیفه‌ای wikidata:Q6934509 freebase:m.03rsv7
dbp:wikiPageUsesTemplate
dbt:Mathcal dbt:Math dbt:Reflist dbt:Div_col dbt:Div_col_end dbt:Math_theorem dbt:Mvar dbt:Short_description dbt:Proof dbt:EquationNote dbt:NumBlk dbt:EquationRef
dbo:abstract
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately. Early versions of MTL were called "hints". In a widely cited 1997 paper, Rich Caruana gave the following characterization: Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better. In the classification context, MTL aims to improve the performance of multiple classification tasks by learning them jointly. One example is a spam-filter, which can be treated as distinct but related classification tasks across different users. To make this more concrete, consider that different people have different distributions of features which distinguish spam emails from legitimate ones, for example an English speaker may find that all emails in Russian are spam, not so for Russian speakers. Yet there is a definite commonality in this classification task across users, for example one common feature might be text related to money transfer. Solving each user's spam classification problem jointly via MTL can let the solutions inform each other and improve performance. Further examples of settings for MTL include multiclass classification and multi-label classification. Multi-task learning works because regularization induced by requiring an algorithm to perform well on a related task can be superior to regularization that prevents overfitting by penalizing all complexity uniformly. One situation where MTL may be particularly helpful is if the tasks share significant commonalities and are generally slightly under sampled. However, as discussed below, MTL has also been shown to be beneficial for learning unrelated tasks.
gold:hypernym
dbr:Approach
prov:wasDerivedFrom
wikipedia-en:Multi-task_learning?oldid=1104310342&ns=0
dbo:wikiPageLength
31255
foaf:isPrimaryTopicOf
wikipedia-en:Multi-task_learning
Subject Item
dbr:Multiclass_classification
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:Multitask_learning
dbo:wikiPageWikiLink
dbr:Multi-task_learning
dbo:wikiPageRedirects
dbr:Multi-task_learning
Subject Item
dbr:Applications_of_multi-task_learning
dbo:wikiPageWikiLink
dbr:Multi-task_learning
dbo:wikiPageRedirects
dbr:Multi-task_learning
Subject Item
dbr:Feature_hashing
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:Federated_learning
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:Kernel_methods_for_vector_output
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:Transfer_learning
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:Matrix_regularization
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:Artificial_general_intelligence
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:SpaCy
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:MTL
dbo:wikiPageWikiLink
dbr:Multi-task_learning
dbo:wikiPageDisambiguates
dbr:Multi-task_learning
Subject Item
dbr:Multitask_optimization
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
dbr:Outline_of_machine_learning
dbo:wikiPageWikiLink
dbr:Multi-task_learning
Subject Item
wikipedia-en:Multi-task_learning
foaf:primaryTopic
dbr:Multi-task_learning