The case demonstrates that CBR has the characteristics of high efficiency and self learning with contrast to conventional Rule Based Reasoning (RBR).
结果表明，与传统的基于规则推理(rbr)相比，CBR具有效率高且有自学习能力的优点。
The learning vector quantization neural network has been integrated successfully with the case-based reasoning approach to reduce the case indexing space and to enhance the indexing efficiency.
将学习矢量量化神经网络集成在基于实例推理的故障诊断方法中，减小了实例搜索空间，提高了实例检索效率。
As to target case given, how to check and choose the most similar case from case-base decides learning and reasoning functions of case-based reasoning system.
对于给定的目标范例，如何从范例库中检索和选择出最为相似的范例决定了范例推理系统的学习与推理性能。
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