About: Kernel methods for vector output

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Kernel methods are a well-established tool to analyze the relationship between input data and the corresponding output of a function. Kernels encapsulate the properties of functions in a computationally efficient way and allow algorithms to easily swap functions of varying complexity. In Gaussian processes, kernels are called covariance functions. Multiple-output functions correspond to considering multiple processes. See Bayesian interpretation of regularization for the connection between the two perspectives.

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rdf:type	software
rdfs:label	Kernel methods for vector output (en)
rdfs:comment	Kernel methods are a well-established tool to analyze the relationship between input data and the corresponding output of a function. Kernels encapsulate the properties of functions in a computationally efficient way and allow algorithms to easily swap functions of varying complexity. In Gaussian processes, kernels are called covariance functions. Multiple-output functions correspond to considering multiple processes. See Bayesian interpretation of regularization for the connection between the two perspectives. (en)
dcterms:subject	Kernel methods for machine learning Machine learning algorithms
Wikipage page ID	42247256 (xsd:integer)
Wikipage revision ID	1098891210 (xsd:integer)
Link from a Wikipage to another Wikipage	Kernel methods for machine learning Kernel methods Bergman kernel Cumulative learning Gaussian process Multi-task learning Cross-validation (statistics) Representer theorem Machine learning Kriging Multi-label classification Machine learning algorithms Transfer learning Laplacian matrix Inductive transfer Curl (mathematics) Bayesian interpretation of regularization Graph kernel Regularization (mathematics) Isometry Covariance function Block matrix Tikhonov regularization Transformation (function) Divergence Kernel trick Eigen decomposition
sameAs	Kernel methods for vector output Kernel methods for vector output Kernel methods for vector output Kernel methods for vector output
dbp:wikiPageUsesTemplate	dbt:Mvar
has abstract	Kernel methods are a well-established tool to analyze the relationship between input data and the corresponding output of a function. Kernels encapsulate the properties of functions in a computationally efficient way and allow algorithms to easily swap functions of varying complexity. In typical machine learning algorithms, these functions produce a scalar output. Recent development of kernel methods for functions with vector-valued output is due, at least in part, to interest in simultaneously solving related problems. Kernels which capture the relationship between the problems allow them to borrow strength from each other. Algorithms of this type include multi-task learning (also called multi-output learning or vector-valued learning), transfer learning, and co-kriging. Multi-label classification can be interpreted as mapping inputs to (binary) coding vectors with length equal to the number of classes. In Gaussian processes, kernels are called covariance functions. Multiple-output functions correspond to considering multiple processes. See Bayesian interpretation of regularization for the connection between the two perspectives. (en)
gold:hypernym	Tool
prov:wasDerivedFrom	wikipedia-en:Kernel_methods_for_vector_output?oldid=1098891210&ns=0
page length (characters) of wiki page	26437 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Kernel_methods_for_vector_output
is Link from a Wikipage to another Wikipage of	Neural tangent kernel Bayesian interpretation of kernel regularization Bayesian quadrature Gaussian process Multi-task learning Kernel method Multi-label classification Outline of machine learning
is foaf:primaryTopic of	wikipedia-en:Kernel_methods_for_vector_output

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