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Handwritten digit recognition by combining support vector machines using rule-based reasoning

机译：通过使用规则的推理来组合支持向量机的手写的数字识别

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摘要

The idea of combining classifiers in order to compensate their individual weakness and to preserve their individual strength has been widely used in recent pattern recognition application* In this paper, the cooperation of two feature families for handwritten digit recognition using SVM (Support Vector Machine) classifiers will be examined. We investigate the advantages and weaknesses of various decision fusion schemes using rule-based reasoning. The obtained results show that it is difficult to exceed the recognition rate of the classifier applied straightforwardly on the feature families as one set. However, the rule-based cooperation schemes enable an easy and efficient implementation of various rejection criteria that leads to high reliability recognition systems.
机译：组合分类器以补偿他们的个人弱点并保持个人实力的想法已被广泛应用于最近的模式识别申请*，在本文中，使用SVM（支持向量机）分类器的两个特征系列的合作将被检查。我们使用基于规则的推理来研究各种决策融合方案的优点和缺点。所得结果表明，作为一个集合，难以超过分类器的识别率在特征家庭上直接应用。然而，基于规则的合作计划能够简单有效地实现各种拒绝标准，导致高可靠性识别系统。

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