A multi-template learning method based on LVQ

A multitemplate learning method based on learning vector quantization (LVQ) is described. In this method, the learning process and the recognition process are carried out alternatively until all of the given data are recognized correctly with an increase in the number of reference vectors. The usefulness of the proposed method is demonstrated through preliminary simulations for artificial data and through recognition experiments for Japanese Hiragana characters compared with the k-means method and conventional LVQ. It is shown that better recognition results are obtained by the proposed method with fewer reference vectors than LVQ