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首页> 外文期刊>Fuzzy sets and systems >Prelude To The Papers 'fuzzy Case Based Reasoning For Facial expression Recognition' And 'temporal Similarity By Measuring possibilistic Uncertainty In Cbr'
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Prelude To The Papers 'fuzzy Case Based Reasoning For Facial expression Recognition' And 'temporal Similarity By Measuring possibilistic Uncertainty In Cbr'

机译：论文“基于模糊案例的面部表情识别推理”和“通过测量Cbr中可能性不确定性的时间相似性”的前奏

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摘要

Longstanding research in artificial intelligence and related fields has produced a number of paradigms for building intelligent and knowledge-based systems, such as rule-based reasoning, constraint processing, or probabilistic graphical models. Being one of these paradigms, case-based reasoning (CBR) has received a great deal of attention in recent years and has been used successfully in diverse application areas. The CBR methodology is inspired by human problem solving and has roots in cognitive psychology. Its key idea is to tackle new problems by resorting to similar problems that have already been solved in the past. More precisely, CBR proceeds from individual experiences in the form of cases. The generalization beyond these experiences in largely founded on principles of analogical reasoning in which the (cognitive) concept of similarity plays an essential role.
机译：人工智能和相关领域的长期研究为构建智能和基于知识的系统提供了许多范例，例如基于规则的推理，约束处理或概率图形模型。基于案例的推理（CBR）作为这些范例之一，近年来受到了广泛的关注，并已成功应用于各种应用领域。社区康复方法论受到人类问题解决的启发，并起源于认知心理学。它的关键思想是通过诉诸过去已经解决的类似问题来解决新问题。更确切地说，社区康复是从案例形式的个人经验中获得的。这些经验的概括主要建立在类比推理的原理上，在类比推理中，（认知）相似性概念起着至关重要的作用。

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    《Fuzzy sets and systems》 |2009年第2期|p.212-213|共2页
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  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 模糊数学;
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