Following the approach of extracting similarity metrics directly from labelled data, a standard back-propagation neural network is adopted to determine a degree of similarity between pairs of input points. The similarity computed by the network is then used to guide a k-NN classifier, which associates a label with an unknown pattern based on the k most similar points. Experimental results on both synthetic and real-world data sets show that the similarity-based k-NN rule outperforms the Euclidean distance-based k-NN rule.
|Autori:||LAZZERINI B.; MARCELLONI F.|
|Titolo:||Classification based on Neural Similarity|
|Anno del prodotto:||2002|
|Digital Object Identifier (DOI):||10.1049/el:20020549|
|Appare nelle tipologie:||1.1 Articolo in rivista|