Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TPAMI.2006.14 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
neural network,clustering,neural networks,learning vector quantization,indexing terms,learning artificial intelligence | Data mining,Data set,Computer science,Vector quantization,Artificial intelligence,Euclidean geometry,Cluster analysis,Artificial neural network,The Internet,Training set,Pattern recognition,Learning vector quantization,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 1 | 0162-8828 |
Citations | PageRank | References |
27 | 1.10 | 15 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos E. Pedreira | 1 | 60 | 6.51 |