Journal Article10.1109/72.655036
Learning in certainty-factor-based multilayer neural networks for classification
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TL;DR: It is shown theoretically that the neural network using the CF-based activation function requires relatively small sample sizes for correct generalization.
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Abstract: 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 requires relatively small sample sizes for correct generalization. This result is also confirmed by empirical studies in several independent domains.
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Citations
Neuro-fuzzy rule generation: survey in soft computing framework
Sushmita Mitra,Yoichi Hayashi +1 more
TL;DR: This article proposes to bring the various neuro-fuzzy models used for rule generation under a unified soft computing framework, and includes both rule extraction and rule refinement in the broader perspective of rule generation.
787
Earlier detection of rumors in online social networks using certainty-factor-based convolutional neural networks
TL;DR: A certainty-factor-based convolutional neural network approach to efficiently classify events as rumor or not by leveraging the inherent features of the set of information in spite of data sparsity is proposed.
43
The application of certainty factors to neural computing for rule discovery
Li Min Fu,Edward H. Shortliffe +1 more
TL;DR: It is a major contribution of this paper to show mathematically the quantizability nature of the CFNet since previously the quantIZability of theCF model was demonstrated only empirically.
43
A neural-network model for learning domain rules based on its activation function characteristics
TL;DR: The CFNet is described, which bases its activation function on the certainty factor (CF) model of expert systems, and a new analysis on the computational complexity of rule learning in general is provided.
34
A Parallel Neural Network Approach for Faster Rumor Identification in Online Social Networks
TL;DR: A twofold convolutional neural network approach with a new activation function which generalizes faster with higher accuracy and detects rumors earlier than other approaches to identify rumors with data sparsity.
24
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