摘要
文章首先分析了传统的实例检索策略的不足，提出了一种基于模糊相似优先比的混合属性实例的检索模型．该模型用语义距离来刻画两实例对应属性之间的相似程度，允许实例的属性为模糊数的情形，能胜任定量。
In this paper, the disadvantages of the existing cases indexing models are analyzed. A new kind of cases indexing model for mixed attributions cases indexing based on fuzzy analogy preferred ratio is presented. This model uses fuzzy distance to describe the similar degree between the corresponding attributions of two cases and is suitable for numerical, linguistic and mixed attributions cases indexing.
出处
《软件学报》
EI
CSCD
北大核心
1999年第5期521-526,共6页
Journal of Software
基金
国家自然科学基金
国家863高科技项目基金
关键词
人工智能
混合属性
实例检索
语义距离
AI(artificial intelligent), CBR(case based reasoning), fuzzy analogy preferred ratio, mixed attribution cases indexing, fuzzy distance.
