Linked Life Data

16792099

Source:http://linkedlifedata.com/resource/pubmed/id/16792099

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2006-6-23
pubmed:abstractText
In order to optimize the accuracy of the Nearest-Neighbor classification rule, a weighted distance is proposed, along with algorithms to automatically learn the corresponding weights. These weights may be specific for each class and feature, for each individual prototype, or for both. The learning algorithms are derived by (approximately) minimizing the Leaving-One-Out classification error of the given training set. The proposed approach is assessed through a series of experiments with UCI/STATLOG corpora, as well as with a more specific task of text classification which entails very sparse data representation and huge dimensionality. In all these experiments, the proposed approach shows a uniformly good behavior, with results comparable to or better than state-of-the-art results published with the same data so far.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0162-8828
pubmed:author
pubmed:issnType
Print
pubmed:volume
28
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1100-10
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Learning weighted metrics to minimize nearest-neighbor classification error.
pubmed:affiliation
Departamento de Sistemas Informáticos y Computación, Instituto Tecnológico de Informática, Universidad Politiécnica de Valencia, Spain. rparedes@iti.upv.es
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't