摘要：In all, 190 articles about neural network learning algorithms published in 1993 and 1994 are examined for the amount of experimental evaluation they contain. Some 29% of them employ not even a single realistic or real learning problem. Only 8% of the articles present results for more than one problem using real world data. Furthermore, one third of all articles do not present any quantitative comparison with a previously known algorithm. These results suggest that we should strive for better assessment practices in neural network learning algorithm research. For the long-term benefit of the field, the publication standards should be raised in this respect and easily accessible collections of benchmark problems should be built.

原文链接:http://www.sciencedirect.com/science/article/pii/0893608095001239

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