High-Dimensional Classification Using Features Annealed Independence Rules
High-Dimensional Classification Using Features Annealed Independence Rules
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Publisher
United States: Institute of Mathematical Statistics
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Language
English
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United States: Institute of Mathematical Statistics
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Contents
Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is poorly understood. In a seminal paper, Bickel and Levina [Bernoulli 10 (2004) 989-1010] show that the Fisher discrim...
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Full title
High-Dimensional Classification Using Features Annealed Independence Rules
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TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_2630123
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_2630123
Other Identifiers
ISSN
0090-5364
E-ISSN
2168-8966
DOI
10.1214/07-AOS504
