Nyström \(M\)-Hilbert-Schmidt Independence Criterion
Nyström \(M\)-Hilbert-Schmidt Independence Criterion
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Kernel techniques are among the most popular and powerful approaches of data science. Among the key features that make kernels ubiquitous are (i) the number of domains they have been designed for, (ii) the Hilbert structure of the function class associated to kernels facilitating their statistical analysis, and (iii) their ability to represent prob...
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Nyström \(M\)-Hilbert-Schmidt Independence Criterion
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TN_cdi_proquest_journals_2778494143
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2778494143
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2331-8422
