摘要
本文提出了一个混合型多概念获取算法HMCAP,它将符号学习CAP算法的主要思想和BP神经网络有机结合,以状态在实例空间出现的概率为启发信息,以提供的混合实例集为分类依据,并具有增量学习能力.HMCAP所依据的实例集既可具有离散属性又可有连续属性,并且能根据用户的要求得到不同精度的结合BP网的二叉多分类判定树.本文还给出HMCAP的算法应用实例,HMCAP可用于自动知识获取系统.
Am ulti-concept acquisition algorithm HMCAP is given, which is a hybrid of symbolic and neural network approaches. HMCAP combines the main idea of CAP and BP neural networks,uses the probability of occurence of a state in the example space as its heuristic information, and with the given amalgamated exam'pie set as its foundation of classification.It also has the ability of incremental learn'ing. The example set may have both discrete and continuous attributes. HMCAP can generate binary multi-classcification trees combining BP networks, of various precision according to user's requirements.The application case of HMCAP is also given. HMCAP can be used in automated knowledge acquisition systems.
出处
《计算机学报》
EI
CSCD
北大核心
1996年第10期753-761,共9页
Chinese Journal of Computers
基金
国家自然科学基金