A criterion for training reference vectors and improved vector quantization
Atsushi Sato, Jun Tsukumo · 1994
In this paper, the criterion for training reference vectors is formulated in which the reference vectors are modified by the input vectors closer to decision boundaries. The authors present an improved vector quantization method, based on the above idea. Decision boundaries determined by this method are discussed and it is shown that the proposed method has several advantages as compared with conventional LVQ2. Experimental results for printed Japanese Hiragana characters recognition reveal that the proposed method is superior to LVQ2 and MLP in recognition ability.>