Knowledge-based connectionism for revising domain theories
Knowledge-based connectionism for revising domain theories
复制标题
用于修订领域理论的基于知识的联结主义
DOI：
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发表时间：
1993
期刊：
影响因子：
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通讯作者：
L. Fu
中科院分区：
文献类型：
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作者：
L. Fu
提出了一种基于知识的机器学习连接主义模型KBCNN。在KBCNN学习模型中，首先识别有用的领域属性和概念，并以与初始领域知识一致的方式进行链接，然后对链接进行适当的加权，以保持语义。隐藏的单位和额外的连接可以被适当地引入到这个初始的连接结构中。然后，这种原始结构进化以最小化经验误差。KBCNN学习模型允许学习或修改的理论被翻译成描述初始理论的基于符号规则的语言。因此，域理论可以被推到网络上，随着时间的推移进行经验性的修正，并以符号形式进行解码。分子遗传学领域被用来证明KBCNN学习模型的有效性及其相对于相关学习方法的优越性。>
A knowledge-based connectionist model for machine learning referred to as KBCNN is presented. In the KBCNN learning model, useful domain attributes and concepts are first identified and linked in a way consistent with initial domain knowledge, and then the links are weighted properly so as to maintain the semantics. Hidden units and additional connections may be introduced into this initial connectionist structure as appropriate. Then, this primitive structure evolves to minimize empirical error. The KBCNN learning model allows the theory learned or revised to be translated into the symbolic rule-based language that describes the initial theory. Thus, a domain theory can be pushed onto the network, revised empirically over time, and decoded in symbolic form. The domain of molecular genetics is used to demonstrate the validity of the KBCNN learning model and its superiority over related learning methods. >