6 April 2000 Knowledge acquisition: neural network learning
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As the amount of information in the world is steadily increasing, there is a growing demand for tools for analyzing the information. Many scholars have been working hard to study machine learning in order to obtain knowledge from domain data sets. They hope to find patterns in terms of implicit dependencies in data. Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Some scholars have done much work to interpret neural networks so that they will no longer be seen as black boxes and provided some plots and methods for knowledge acquisition using neural networks. These can be classified into three categories: fuzzy neural networks, CF (certainty factor) based neural networks, and logical neurons. We review some of these research works in this paper.
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Guoyin Wang, Guoyin Wang, Paul S. Fisher, Paul S. Fisher, } "Knowledge acquisition: neural network learning", Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); doi: 10.1117/12.381724; https://doi.org/10.1117/12.381724


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