Linked Life Data

19963901

Source:http://linkedlifedata.com/resource/pubmed/id/19963901

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2009-12-7
pubmed:abstractText
This work examines support vector machine (SVM) classification of complex fMRI data, both in the image domain and in the acquired k-space data. We achieve high classification accuracy using the magnitude data in both domains. Additionally, we maintain high classification accuracy even when using only partial k-space data. Thus we demonstrate the feasibility of using kspace data for classification, enabling rapid realtime acquisition and classification.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
2009
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
5381-4
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Support vector machine classification of complex fMRI data.
pubmed:affiliation
Functional MRI Laboratory, University of Michigan, Ann Arbor, MI 48109, USA. spelt@umich.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural