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Abstract: Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, more often than not, explicit knowledge is needed by human experts. This work drives a symbolic representation for neural networks to make explicit each prediction of a neural network. An algorithm is proposed and implemented to... (Update)
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.... in mind as a prior objective, a number of researchers have applied the method of extracting Boolean rules from neural networks [160 162, 169]. Their results are encouraging, exhibiting both good performance and a reduced number of rules and relevant input variables....
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BibTeX entry: (Update)
R. Setiono and H. Liu. Symbolic representation of neural networks. IEEE Computer Magzine, pages 71--77, 1996. http://citeseer.ist.psu.edu/article/setiono96symbolic.html More
@article{ setiono96symbolic,
author = "Rudy Setiono and Huan Liu",
title = "Symbolic Representation of Neural Networks",
journal = "IEEE Computer",
volume = "29",
number = "3",
pages = "71-77",
year = "1996",
url = "citeseer.ist.psu.edu/article/setiono96symbolic.html" }
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