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Traditionally, human experts have derived their knowledge from their own personal observation and experience. With advancing computer technology, automated knowledge discovery has become an important AI research topic, as well as a practical business application in an increasing number of organizations. Knowledge discovery can be defined as the learning of(More)
How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a new incremental learning method for pattern recognition, called the "incremental backpropagation learning network", which employs bounded weight modification and structural adaptation learning rules and(More)
In expert systems, hierarchical reasoning can provide better accuracy and understandability. Here, we develop a method of learning hierarchical knowledge from a case library, in which each training instance is described by low level features and high level concepts (e.g., manifestations and diseases) but not by intermediate concepts (e.g., disease states).(More)
A major development in knowledge-based neural networks is the integration of symbolic expert rule-based knowledge into neural networks, resulting in so-called rule-based neural (or connectionist) networks. An expert network here refers to a particular construct in which the uncertainty management model of symbolic expert systems is mapped into the(More)
The computational framework of rule-based neural networks inherits from the neural network and the inference engine of an expert system. In one approach, the network activation function is based on the certainty factor (CF) model of MYCIN-like systems. In this paper, it is shown theoretically that the neural network using the CF-based activation function(More)