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dc.contributor.author
Cholaquidis, A.
dc.contributor.author
Fraiman, R.
dc.contributor.author
Sued, Raquel Mariela
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dc.date.available
2021-11-26T13:41:26Z
dc.date.issued
2020-12
dc.identifier.citation
Cholaquidis, A.; Fraiman, R.; Sued, Raquel Mariela; On semi-supervised learning; Springer; Test; 29; 4; 12-2020; 914-937
dc.identifier.issn
1133-0686
dc.identifier.uri
http://hdl.handle.net/11336/147485
dc.description.abstract
Major efforts have been made, mostly in the machine learning literature, to construct good predictors combining unlabelled and labelled data. These methods are known as semi-supervised. They deal with the problem of how to take advantage, if possible, of a huge amount of unlabelled data to perform classification in situations where there are few labelled data. This is not always feasible: it depends on the possibility to infer the labels from the unlabelled data distribution. Nevertheless, several algorithms have been proposed recently. In this work, we present a new method that, under almost necessary conditions, attains asymptotically the performance of the best theoretical rule when the size of the unlabelled sample goes to infinity, even if the size of the labelled sample remains fixed. Its performance and computational time are assessed through simulations and in the well- known “Isolet” real data of phonemes, where a strong dependence on the choice of the initial training sample is shown. The main focus of this work is to elucidate when and why semi-supervised learning works in the asymptotic regime described above. The set of necessary assumptions, although reasonable, show that semi-parametric methods only attain consistency for very well-conditioned problems.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
CONSISTENCY
dc.subject
SEMI-SUPERVISED LEARNING
dc.subject
SMALL TRAINING SAMPLE
dc.subject.classification
Estadística y Probabilidad
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dc.subject.classification
Matemáticas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
On semi-supervised learning
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-12-09T20:15:51Z
dc.journal.volume
29
dc.journal.number
4
dc.journal.pagination
914-937
dc.journal.pais
Alemania
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dc.journal.ciudad
Berlin
dc.description.fil
Fil: Cholaquidis, A.. Universidad de la República; Uruguay
dc.description.fil
Fil: Fraiman, R.. Universidad de la República; Uruguay
dc.description.fil
Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Test
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s11749-019-00690-2
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs11749-019-00690-2
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1805.09180
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