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A Proposal of Temporal Case-Base Maintenance Algorithms

机译:临时案例库维护算法的建议

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Time plays a key role in describing stories and cases. In the last decades, great efforts have been done to develop Case-Based Reasoning (CBR) systems that can cope with the temporal dimension. Despite this kind of system finds it difficult to maintain their case-base, little attention has been paid to maintain temporal case-bases. Case-Base Maintenance (CBM) algorithms are useful tools to build efficient and reliable CBR systems. In this work, we propose the extension of different CBM approaches to deal with this problem. Five temporal CBM algorithms are proposed (t-CNN, t-RENN, t-DROP1, t-ICF and t-RC-FP). These algorithms make the maintenance of case-bases possible when cases are temporal event sequences.
机译:时间在描述故事和案例方面发挥着关键作用。在过去的几十年中,已经努力制定了可以应对时间维度的案例的推理(CBR)系统。尽管这种系统发现难以保持其案例基础,但很少关注维持时间案例基础。案例基础维护(CBM)算法是构建高效可靠的CBR系统的有用工具。在这项工作中,我们建议延伸不同的CBM方法来处理这个问题。提出了五个时间CBM算法(T-CNN,T-RENN,T-DAPL1,T-ICF和T-RC-FP)。当案例是时间事件序列时,这些算法使案例基础的维护成为可能。

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