Edited Nearest Neighbor Rule for Improving Neural Networks Classifications
Impacto
Scholar |
Otros documentos de la autoría: Alejo Eleuterio, Roberto; Martínez Sotoca, José; Valdovinos Rosas, Rosa María; Toribio Luna, Primitivo
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/54899
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http://dx.doi.org/10.1007/978-3-642-13278-0_39 |
Metadatos
Título
Edited Nearest Neighbor Rule for Improving Neural Networks ClassificationsAutoría
Fecha de publicación
2010Editor
Springer Berlin HeidelbergISBN
978-3-642-13277-3ISSN
0302-9743Cita bibliográfica
Advances in Neural Networks - ISNN 2010. Berlin: Springer Berlin Heidelberg, 2010, p. 303-310Tipo de documento
info:eu-repo/semantics/bookPartVersión de la editorial
http://link.springer.com/chapter/10.1007/978-3-642-13278-0_39Palabras clave / Materias
Resumen
The quality and size of the training data sets is a critical stage on the ability of the artificial neural networks to generalize the characteristics of the training examples. Several approaches are focused to form ... [+]
The quality and size of the training data sets is a critical stage on the ability of the artificial neural networks to generalize the characteristics of the training examples. Several approaches are focused to form training data sets by identification of border examples or core examples with the aim to improve the accuracy of network classification and generalization. However, a refinement of data sets by the elimination of outliers examples may increase the accuracy too. In this paper, we analyze the use of different editing schemes based on nearest neighbor rule on the most popular neural networks architectures. [-]
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