通过分类正确度有效处理了决策表的不一致性，采用启发式算法，挖掘出满足给定精确度的最简产生式规则知识。
We deal with the inconsistency through classification accuracy, using heuristic algorithms we can get a set of minimal productive rules satisfying the given classification accuracy.
此外，启发式知识应加以开发去指导控制的动态层次分布。
Besides, some heuristic knowledge should be developed to guide the dynamic hierarchical distribution of the control.
神经元网络启发式的并行、分布特征和可学习性为专家系统的知识表达和获取、不确定性推理提供了新的途径。
The parallelism, distribution and capability of learning of neural net heuristics provide a new way for knowledge acquisition, knowledge representation and uncertainty reasoning in expert systems.
提出了一种基于条件信息熵的知识约简启发式算法，并指出该算法的时间复杂度是多项式的。
A heuristic algorithm based on conditional information entropy for knowledge reduction is proposed, and the complexity of this algorithm is analyzed.
该算法利用问题的邻域知识指导局部搜索，可克服元启发式算法随机性引起的盲目搜索。
The proposed algorithm utilizes neighborhood knowledge to direct its local search procedure which can overcome the blindness or randomness introduced by meta-heuristics.
专家系统中的知识采用启发式表示，知识可以表示成确定性和不确定性知识。
The knowledge in the ES adopts the production systems, which can express the certain and uncertain knowledge.
人们在相关信息不足、知识和时间都有限制的情况下，的确会采用启发式再认策略进行推理，而且启发式再认使用率达到相当高的水平。
People most use Recognition Heuristic inference strategy when knowledge was missing, time limited and lake of information. And their frequencies are up to the very high level.
本文采用口语报告分析所进行的研究表明该框架是一种问题求解方面的知识结构和有效的启发式求解策略。
The study used the protocol analysis showed that the framework is a kind of knowledge structure and an effective heuristic strategy of problem solving.
基于解释的分类算法可纳入知识工程中启发式分类的范畴。
A classification algorithm based on explanation could be put into the category of heuristic classification in knowledge engineering.
现代启发式教学作为一种与知识传授教学相对应的教学，在世界范围内日益受到重视。
Heuristics, which is valued world wide is the opposite teaching method of attaching importance to teaching the students knowledge.
本文提出一种将第一原理与启发式知识有机结合的模型，并给出了相应的算法。
This paper provides a model using both the deep and shallow knowledges, and presents some algorithms.
本文提出一种将第一原理与启发式知识有机结合的模型，并给出了相应的算法。
This paper provides a model using both the deep and shallow knowledges, and presents some algorithms.
应用推荐