Abstract
In this paper, a set of neighbourhood-based classifiers are jointly used in order to select a more reliable neighbourhood of a given sample and take an appropriate decision about its class membership. The approaches introduced here make use of two concepts: proximity and symmetric placement of the samples.
This work was partially supported by grants P1A94-23, P1B96-13 (Fundació Caixa-Castelló-Bancaixa), GV2110/94 (Conselleria d'Educació i Ciència), AGF95-0712-C03-01 and TIC95-676-C02-01 (Spanish CICYT).
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Keywords
- Classification Accuracy
- Single Classifier
- Pattern Recognition Letter
- Minimum Classification Error
- Multiple Classifier System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 1997 Springer-Verlag Berlin Heidelberg
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Sánchez, J.S., Pla, F., Ferri, F.J. (1997). Using proximity and spatial homogeneity in neighbourhood-based classifiers. In: Del Bimbo, A. (eds) Image Analysis and Processing. ICIAP 1997. Lecture Notes in Computer Science, vol 1310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63507-6_203
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DOI: https://doi.org/10.1007/3-540-63507-6_203
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