Modeling High-Dimensional Data.
The article discusses modeling high-dimensional data, discussing the use of Gaussian distributions. The article discusses how Gaussian distributions are estimated using the covariance matrix of data and examines an article in the issue by Kalai, Moitra, and Valiant in which they discuss how to solve...
| Publicado en: | Communications of the ACM Vol. 55; no. 2; pp. 112 - 113 |
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| Autor principal: | |
| Formato: | Opinion |
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Association for Computing Machinery
Feb2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=71681510&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 71681510 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Feb2012 vid: 55 iid: 2 pid: 68 pub: Association for Computing Machinery artinfo: ui: 71681510 10.1145/2076450.2076473 ppf: 112 ppct: 1 formats: tig: atl: Modeling High-Dimensional Data. aug: au: Vempala, Santosh S. su: Gaussian distribution Data modeling Distribution (Probability theory) Multidimensional databases sug: subj: Gaussian distribution Data modeling Distribution (Probability theory) Multidimensional databases ab: The article discusses modeling high-dimensional data, discussing the use of Gaussian distributions. The article discusses how Gaussian distributions are estimated using the covariance matrix of data and examines an article in the issue by Kalai, Moitra, and Valiant in which they discuss how to solve a mixture with two arbitrary multidimensional Gaussians. The author argues that the paper uses an ingenious reduction to solve the problem. pubtype: Periodical doctype: Opinion src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2012 holdings: @attributes: islocal: N |
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