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中文摘要：
支持向量机（SVM）在解决小样本、非线性及高维模式识别问题等方面有许多优势,但在处理大规模数据集时训练速度缓慢。针对以上问题提出了SVM学习算法硬件化的设计,它可以在保证向量机学习速度的同时,提高支持向量机的硬件资源利用效率。ECT图像重建实验结果表明,在不影响分类精度的情况下,硬件实现有效减少了运行时间,在一些实时性要求较高的场合该方法的优点将尤为明显。
英文摘要：
Support vector machine（SVM） presents excellent performance to solve the problems with small sample,nonlinear and the problems of high-dimension pattern recognition,but it has slow training speed to deal with large amount of data.So,a new digital structure for SVM learning,which can get a good performance with less hardware resource while the process speed is not reduced.The ECT image reconstruction experimental results show that the hardware implementation of the algorithm reduces the running time efficiently,not decreasing the classification accuracy,and the advantages of this method will be particularly significant in occasions of realtime requirements.
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