Linked Life Data

11088612

Source:http://linkedlifedata.com/resource/pubmed/id/11088612

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1 Pt B
pubmed:dateCreated
2001-1-8
pubmed:abstractText
Supervised online learning with an ensemble of students randomized by the choice of initial conditions is analyzed. For the case of the perceptron learning rule, asymptotically the same improvement in the generalization error of the ensemble compared to the performance of a single student is found as in Gibbs learning. For more optimized learning rules, however, using an ensemble yields no improvement. This is explained by showing that for any learning rule f a transform f exists, such that a single student using f has the same generalization behavior as an ensemble of f students.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1063-651X
pubmed:author
pubmed:issnType
Print
pubmed:volume
62
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1448-51
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Online learning with ensembles.
pubmed:affiliation
Neural Computing Research Group, Aston University, Aston Triangle, Birmingham B4 7ET, United Kingdom.
pubmed:publicationType
Journal Article, Clinical Trial