会议论文详细信息
1st International Workshop on Combinations of Intelligent Methods and Applications
Improving the Accuracy of Neuro-Symbolic Rules withCase-Based Reasoning
Jim Prentzas ; Ioannis Hatzilygeroudis ; Othon Michail
Others  :  http://CEUR-WS.org/Vol-375/paper9.pdf
PID  :  46814
来源: CEUR
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【 摘 要 】

In this paper, we present an improved approach integrating rules, neural networks and cases, compared to aprevious one. The main approach integrates neurules and cases.Neurules are a kind of integrated rules that combine a symbolic (production rules) and a connectionist (adaline unit)representation. Each neurule is represented as an adaline unit. The main characteristics of neurules are that they improve theperformance of symbolic rules and, in contrast to other hybridneuro-symbolic approaches, retain the modularity of production rules and their naturalness in a large degree. In the improved approach, various types of indices are assigned to cases according to different roles they play in neurule-based reasoning, instead of one. Thus, an enhanced knowledgerepresentation scheme is derived resulting in accuracyimprovement. Experimental results demonstrate its effectiveness.

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