摘要
为了支持在事实不完全或不充分环境中的有效推理，作者提出了一种归纳机器学习方法，并设计了一个规则向量投影算法，使用木文介绍的算法可对原始知识实行归纳，生成含一系列全新分类概念和推理路经的网络知识库，基于该知识库的机器推理系统，在作出诊断决策时所需事实量可大为减少，因此在信息量不足的情况下仍能具有很高的推理性能．
To support the effective reasoning in the circumstances of incomplete andinsufficient facts, the author presented an inductive machine learning method, and designed a rule vector projection algorithm. With the method implemented by the algorithmthe primitive knowledge are processed by inductive approach, created a network knowledge base full of new classification concepts and paths for reasoning. Based on the knowledge base the quantity of facts needed in a diagnostic decision making by a machine inference system can sharply be reduced, thus a high performance can still be achieved by thesystem, despite of short information.
出处
《软件学报》
EI
CSCD
北大核心
1996年第6期364-370,共7页
Journal of Software
关键词
机器学习
知识库
人工智能
归纳机器学习
RVPA
Expert system,machine learning,inductive learning, knowledge base, classification.
