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.

Attributes	Values
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)
sameAs	Kernel methods for vector output Kernel methods for vector output
dbp:wikiPageUsesTemplate	dbt:Mvar
Subject	Kernel methods for machine learning Machine learning algorithms
gold:hypernym	Pack saddle
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Kernel_methods_for_vector_output?oldid=969570782&ns=0
Wikipage page ID	42247256 (xsd:integer)
page length (characters) of wiki page	26377 (xsd:nonNegativeInteger)
Wikipage revision ID	969570782 (xsd:integer)
Link from a Wikipage to another Wikipage	Machine learning Bayesian interpretation of kernel regularization Block matrix Tikhonov regularization Kernel methods for machine learning Machine learning algorithms Divergence Cross-validation (statistics) Cumulative learning Curl (mathematics) Eigendecomposition of a matrix Covariance function Gaussian process Graph kernel Kernel method Kriging Transfer learning Transformation (function) Isometry Laplacian matrix Multi-label classification Meta learning Regularization (mathematics) Representer theorem Bergman kernel
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)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Kernel_methods_for_vector_output
is Link from a Wikipage to another Wikipage of	Bayesian interpretation of kernel regularization Bayesian quadrature Gaussian process Kernel method Multi-label classification Meta learning Neural tangent kernel
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Kernel_methods_for_vector_output

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