In order to improve classification efficiency of multiclass pattern recognition based on " one a-gainst one" learning algorithm in vector machine, investigated the method of support vector machine and relevance vector machine algorithm in multi-mode classification, and found that comparison for too many times was the main reason for large amount of calculation. Proposed a way that eliminated the most dissimilar class in each round of comparison. Comparison times were reduced step by step per cycle. The classification number was more, and the decrease in the total calculation amount was more obvious. The theory analysis and the experimental results of data classification show that compared with traditional classifier , the training times and the recognition times of the method are greatly reduced under the premise of hardly influencing classification accuracy, and the algorithm running speed is improved obviously.%为了提高向量机“一对一”学习算法在多模式识别中的分类效率,对基于支持向量机和相关向量机算法进行多模式分类的方法进行研究,发现比较次数过多是该方法计算量大的主要原因.提出了一种在每轮比较中,排除最差类别的新方法.该方法使比较次数逐级减少,并且当类别数较多时,总计算量减少尤其明显.通过理论分析和对数据分类的实验结果表明,新方法与传统分类器相比,在基本不影响分类正确率的前提下,机器训练与识别次数显著减少,算法运行速度明显提高.
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