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曾晓勤, 曾晓勤+, 韩秀清, 邹阳
软件学报,2008,19(8):1893~1901,-0001,():
-1年11月30日
围绕解决图文法中的主要问题——嵌入问题,提出了一种基于边的上下文相关图文法形式化框架,并对由此定义的文法的一些性质及相应的归约算法进行了讨论。对所提出的图文法与已有的文法进行了比较。同时,展望了今后值得进一步研究的一些问题和方向。
可视化语言, 形式化, 图文法, 嵌入问题, 产生式
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曾晓勤, Xiaoqin
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 6, NOVEMBER 2001,-0001,():
-1年11月30日
An
Multilayer
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曾晓勤, Xiaoqin
X. Zeng, D. S. Yeung. Neurocomputing 69 (2006) 825-837,-0001,():
-1年11月30日
In
Neural
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曾晓勤, Xiaoqin
,-0001,():
-1年11月30日
This paper presents an approach to determine the relevance of individual input attributes for trained Multilayer Perceptrons (MLPs). To reflect the impact of an input attribute on the output of an MLP, the relevance is aimed at representing the output sensitivity of the MLP to the attribute variation. The sensitivity is defined as the mathematical expectation of output deviations of an MLP due to its input deviation with respect to overall input patterns. The basic idea for the introduction of such a relevance measure is that a well-trained MLP can capture salient features of the problem it deals with and thus become more sensitive to those input attributes that make more contributions to the MLP’s behavior. The relevance can be employed as a relative criterion for assessing individual input attributes. The results from the experiments on two typical problems demonstrate the effectiveness of the relevance in identifying irrelevant input attribute.
Neural
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