Premature clustering phenomenon and new training algorithms for LVQ

摘要

Five existing LVQ algorithms are reviewed. The Premature Clustering Phenomenon, which downgrades the performance of LVQ is explained. By introducing and applying the “equalizing factor” as a remedy for the premature clustering phenomenon a breakthrough is achieved in improving the performance of the LVQ network, and its performance becomes competitive with that of the best known classifiers. For estimating the equalizing factor four different formulas are suggested, which result in four different versions of the LVQ4a algorithm. A new weight-updating formula for LVQ is presented, and the LVQ4b algorithm is presented as implementation of this new weight-updating formula in batch mode training. In addition, four variants of the LVQ4c algorithm are presented as the customized LVQ4b algorithm for pattern mode training.A meticulous analysis of their performances and that of five early training algorithms has been carried out and they have been compared against each other, on 16 databases of the Farsi optical character recognition problem.