SVM置信度在线评估以及决策改进

For SVM:confidence online evaluation & decision impro-vement

设计了SVM置信度在线评估方案,以此确定SVM做多分类时结果的风险程度,对高风险决策结果进行修正。置信度评估采用理论估计和经验估计相结合的方式。多分类决策结果的修正由在线生成的局部分类器完成。局部分类器在待查询数据的邻域内工作,此邻域基于一个局部测度而生成。实验表明,所设计的算法呈现了较好的分类能力,提高了传统同类方法的分类准确率。

An algorithm of confidence evaluation for SVM is presented. Based on the evaluation, decision risk is specified in the context of multi-classification. Evaluation approach combines the theoretic analysis and empirical analysis. The decision with low confidence is refined by a local classifier that is formulated online. The local classifier works in query＇s neighborhood,and the neighborhood is developed according to a local metric. Experiments demonstrate the fine performance of the designed algorithm, and its improvement in classification accuracy over the state of the arts.