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ICPR
2000
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Weighting Prototypes. A New Editing Approach

8 years 11 months ago
Weighting Prototypes. A New Editing Approach
It is well known that editing techniques can be applied to (large) sets of prototypes in order to bring the error rate of the Nearest Neighbour classifier close to the optimal Bayes risk. However, in practice, the behaviour of these techniques uses to be much worse than expected from the asymptotic predictions. A novel editing technique is introduced here which explicitly aims at obtaining a good editing rule for each given prototype set. This is achieved by first learning an adequate assignment of a weight to each prototype and then pruning out those prototypes having large weights. Experiments are presented which clearly show the superiority of this new method, specially for small data sets and/or large dimensions.
Roberto Paredes Palacios, Enrique Vidal
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where ICPR
Authors Roberto Paredes Palacios, Enrique Vidal
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