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 | |
rdfs:label |
|
rdfs:comment |
|
dcterms:subject | |
Wikipage page ID |
|
Wikipage revision ID |
|
Link from a Wikipage to another Wikipage |
|
sameAs | |
dbp:wikiPageUsesTemplate | |
has abstract |
|
gold:hypernym | |
prov:wasDerivedFrom | |
page length (characters) of wiki page |
|
foaf:isPrimaryTopicOf | |
is Link from a Wikipage to another Wikipage of | |
is foaf:primaryTopic of |