18252427

Source:http://linkedlifedata.com/resource/pubmed/id/18252427

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0036576, umls-concept:C0205177, umls-concept:C0449774, umls-concept:C0681935
pubmed:issue	1
pubmed:dateCreated	2008-2-6
pubmed:abstractText	We propose a new approach for leave-one-out cross-validation of neural-network classifiers called "cross-validation with active pattern selection" (CV/APS). In CV/APS, the contribution of the training patterns to network learning is estimated and this information is used for active selection of CV patterns. On the tested examples, the computational cost of CV can be drastically reduced with only small or no errors.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/101211035
pubmed:status	PubMed-not-MEDLINE
pubmed:issn	1045-9227
pubmed:author	pubmed-author:HornikKK, pubmed-author:JainL CLC, pubmed-author:LeischFF
pubmed:issnType	Print
pubmed:volume	9
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	35-41
pubmed:year	1998
pubmed:articleTitle	Cross-validation with active pattern selection for neural-network classifiers.
pubmed:affiliation	Institut für Statistik und Wahrscheinlichkeitstheorie, Technische Universität Wien, A-1040 Wien, Austria.
pubmed:publicationType	Journal Article