LVQ COMBINED WITH SIMULATED ANNEALING FOR OPTIMAL-DESIGN OF LARGE-SETREFERENCE MODELS

Learning Vector Quantization (LVQ) has been intensively studied to gen erate good reference models in pattern recognition since 1986, and it has some nice theoretical properties. However, the design of reference models based on LVQ suffers from several major drawbacks for the reco gnition of large-set patterns, in which good reference models play an important role in achieving high performance. They are due in large pa rt to the following facts. (1) it may not generate good reference mode ls, if the initial values of the reference models are outside the conv ex hull of the input data, (2) it cannot guarantee optimal reference m odels due to the strategy to accept new reference models in each itera tion step, and (3) it is apt to get stuck at overtraining phenomenon. In this paper, we first discuss the impact of these problems. And then , to cope with these, we propose a new method for the optimal design o f large-set reference models using an improved LVQ3 combined with Simu lated Annealing which has been proven to be a useful technique in many areas of optimization problems. Experimental results with large-set h andwritten characters reveal that the proposed method is superior to t he conventional method based on averaging and other LVQ-based methods.