Learning vector quantization with training data selection.
In this paper, we propose a method that selects a subset of the training data points to update LVQ prototypes. The main goal is to conduct the prototypes to converge at a more convenient location, diminishing misclassification errors. The method selects an update set composed by a subset of points considered to be at the risk of being captured by another class prototype. We associate the proposed methodology to a weighted norm, instead of the Euclidean, in order to establish different levels of relevance for the input attributes. The technique was implemented on a controlled experiment and on Web available data sets.
Pedreira
Carlos E
CE
Federal University of Rio de Janeiro-UFRJ, Rio de Janeiro, Brazil. carlosp@centroin.com.br
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003205:Computing Methodologies; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D013598:Systems Theory