Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2006-10-23
pubmed:abstractText
We previously reported the use of clustering by unsupervised learning with machine learning classifiers to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma. In this study, the process of unsupervised learning by independent component analysis decomposed SAP field patterns into axes, and the information represented by these axes was evaluated.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1545-6110
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
103
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
270-80
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects.
pubmed:affiliation
Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural