About: Multiple kernel learning

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Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels, reducing bias due to kernel selection while allowing for more automated machine learning methods, and b) combining data from different sources (e.g. sound and images from a video) that have different notions of similarity and thus require different kernels. Instead of creating a new kernel, multiple kernel algorithms can be used to combine kernels already established for each individual data source.

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rdfs:label	Multiple kernel learning (en)
rdfs:comment	Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels, reducing bias due to kernel selection while allowing for more automated machine learning methods, and b) combining data from different sources (e.g. sound and images from a video) that have different notions of similarity and thus require different kernels. Instead of creating a new kernel, multiple kernel algorithms can be used to combine kernels already established for each individual data source. (en)
sameAs	Multiple kernel learning
dbp:wikiPageUsesTemplate	dbt:Reflist dbt:Machine_learning_bar
Subject	Data mining Machine learning algorithms
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Multiple_kernel_learning?oldid=1062901989&ns=0
Wikipage page ID	44635680 (xsd:integer)
page length (characters) of wiki page	16270 (xsd:nonNegativeInteger)
Wikipage revision ID	1062901989 (xsd:integer)
Link from a Wikipage to another Wikipage	Proximal gradient methods for learning Support-vector machine Semi-supervised learning Tikhonov regularization Unsupervised learning Data mining Gibbs sampling Machine learning algorithms Kullback–Leibler divergence Elastic net regularization Gradient descent Kernel method Structural risk minimization MATLAB Multinomial probit Bergman kernel
has abstract	Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels, reducing bias due to kernel selection while allowing for more automated machine learning methods, and b) combining data from different sources (e.g. sound and images from a video) that have different notions of similarity and thus require different kernels. Instead of creating a new kernel, multiple kernel algorithms can be used to combine kernels already established for each individual data source. Multiple kernel learning approaches have been used in many applications, such as event recognition in video, object recognition in images, and biomedical data fusion. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Multiple_kernel_learning
is Wikipage redirect of	Unsupervised multiple kernel learning Algorithms for multiple kernel learning
is Link from a Wikipage to another Wikipage of	MKL Matrix regularization Algorithms for multiple kernel learning
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Multiple_kernel_learning

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