In this paper, we propose a fast learning algorithm of a support vector machine (SVM). Our work is based on the Learning Vector Quantization (LVQ) and we compress the data to perform properly in the context of clustered data margin maximization. For solving the problem faster, we propose the improved TOD algorithm, which is one of the simplest form of LVQ. Experimental results demonstrate that our method is as accurate as the existing implementation, but it is faster in most situations. We also show the extension of the proposed learning framework for online re-training problem.
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