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- Multiple_kernel_learning 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.".
- Multiple_kernel_learning wikiPageExternalLink doku.php?id=gmkl.
- Multiple_kernel_learning wikiPageExternalLink mklindex.html.
- Multiple_kernel_learning wikiPageExternalLink download.html.
- Multiple_kernel_learning wikiPageExternalLink download.html.
- Multiple_kernel_learning wikiPageExternalLink download.html.
- Multiple_kernel_learning wikiPageID "44635680".
- Multiple_kernel_learning wikiPageLength "15822".
- Multiple_kernel_learning wikiPageOutDegree "15".
- Multiple_kernel_learning wikiPageRevisionID "705383147".
- Multiple_kernel_learning wikiPageWikiLink Category:Data_mining.
- Multiple_kernel_learning wikiPageWikiLink Category:Machine_learning_algorithms.
- Multiple_kernel_learning wikiPageWikiLink Elastic_net_regularization.
- Multiple_kernel_learning wikiPageWikiLink Gibbs_sampling.
- Multiple_kernel_learning wikiPageWikiLink Kernel_method.
- Multiple_kernel_learning wikiPageWikiLink Kullback–Leibler_divergence.
- Multiple_kernel_learning wikiPageWikiLink MATLAB.
- Multiple_kernel_learning wikiPageWikiLink Multinomial_probit.
- Multiple_kernel_learning wikiPageWikiLink Proximal_gradient_methods_for_learning.
- Multiple_kernel_learning wikiPageWikiLink Reproducing_kernel_Hilbert_space.
- Multiple_kernel_learning wikiPageWikiLink Semi-supervised_learning.
- Multiple_kernel_learning wikiPageWikiLink Structural_risk_minimization.
- Multiple_kernel_learning wikiPageWikiLink Support_vector_machine.
- Multiple_kernel_learning wikiPageWikiLink Tikhonov_regularization.
- Multiple_kernel_learning wikiPageWikiLink Unsupervised_learning.
- Multiple_kernel_learning wikiPageWikiLinkText "Multiple kernel learning".
- Multiple_kernel_learning wikiPageWikiLinkText "multiple kernel learning".
- Multiple_kernel_learning wikiPageUsesTemplate Template:Machine_learning_bar.
- Multiple_kernel_learning wikiPageUsesTemplate Template:Reflist.
- Multiple_kernel_learning subject Category:Data_mining.
- Multiple_kernel_learning subject Category:Machine_learning_algorithms.
- Multiple_kernel_learning 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.".
- Multiple_kernel_learning label "Multiple kernel learning".
- Multiple_kernel_learning sameAs m.012nvxjf.
- Multiple_kernel_learning wasDerivedFrom Multiple_kernel_learning?oldid=705383147.
- Multiple_kernel_learning isPrimaryTopicOf Multiple_kernel_learning.