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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1392| A new fast prototype selection method based on clustering | |
| JOSE ARTURO OLVERA LOPEZ JESUS ARIEL CARRASCO OCHOA JOSE FRANCISCO MARTINEZ TRINIDAD | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Prototype selection Supervised classification Instance-based classifiers Border prototypes Data reduction Clustering | |
| In supervised classification, a training set T is given to a classifier for classifying new prototypes. In practice, not all information in T is useful for classifiers, therefore, it is convenient to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time for classification or training could be reduced. In this work, we propose a new fast prototype selection method for large datasets, based on clustering, which selects border prototypes and some interior prototypes. Experimental results showing the performance of our method and comparing accuracy and runtimes against other prototype selection methods are reported. | |
| Springer-Verlag London Limited | |
| 2010 | |
| Artículo | |
| Inglés | |
| Estudiantes Investigadores Público en general | |
| Olvera-López, J.A., et al., (2010). A new fast prototype selection method based on clustering, Pattern Analysis and Applications, (13): 131–141 | |
| CIENCIA DE LOS ORDENADORES | |
| Versión aceptada | |
| acceptedVersion - Versión aceptada | |
| Aparece en las colecciones: | Artículos de Ciencias Computacionales |
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