Modelling contextual constraints in probabilistic relaxation for multi-class semi-supervised learning
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Scholar |
Otros documentos de la autoría: Martínez Usó, Adolfo; Pla, Filiberto; Martínez Sotoca, José
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comunitat-uji-handle2:10234/43662
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http://dx.doi.org/10.1016/j.knosys.2014.04.023 |
Metadatos
Título
Modelling contextual constraints in probabilistic relaxation for multi-class semi-supervised learningFecha de publicación
2014Editor
ElsevierISSN
0950-7051; 1872-7409Cita bibliográfica
MARTÍNEZ-USÓ, Adolfo; PLA, Filiberto; SOTOCA, José M. Modelling contextual constraints in probabilistic relaxation for multi-class semi-supervised learning. Knowledge-Based Systems, 2014, vol. 66, p. 82-91.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0950705114001452Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This paper proposes a semi-supervised approach based on probabilistic relaxation theory. The algorithm performs a consistent multi-class assignment of labels according to the contextual information constraints. We ... [+]
This paper proposes a semi-supervised approach based on probabilistic relaxation theory. The algorithm performs a consistent multi-class assignment of labels according to the contextual information constraints. We start from a fully connected graph where each initial sample of the input data is a node of the graph and where only a few nodes have been labelled. A local propagation process is then performed by means of a support function where a new compatibility measure has been proposed. Contributions also include a comparative study of a wide variety of data sets with recent and well-known state-of-the-art algorithms for semi-supervised learning. The results have been provided by an analysis of their statistical significance. Our methodology has demonstrated a noticeably better performance in multi-class classification tasks. Experiments will also show that the proposed technique could be especially useful for applications such as hyperspectral image classification. [-]
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Knowledge-Based Systems, 2014, vol. 66Derechos de acceso
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