Untangling Neural Nets
Untangling Neural Nets
复制标题
解开神经网络
DOI：
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复制
发表时间：
2004
影响因子：
6.8
通讯作者：
Jeffrey D. Grynaviski
中科院分区：
文献类型：
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作者：
S. Marchi;Christopher Gelpi;Jeffrey D. Grynaviski
Beck，King和Zeng(2000)对定量安全研究领域提出了全面的批评，并为未来的研究提供了一个大胆的新方向。尽管他们的工作具有重要的优势，但我们对他们的研究的三个方面提出了异议：(1)他们将Logit模型与他们的神经网络进行比较的实质；(2)他们用来评估预测的标准；(3)以神经网络为代表的非参数方法的理论和建模含义。我们通过估计一个更完整的Logit模型，并将其与神经网络和线性判别分析进行比较，来复制和扩展他们的分析。我们的工作表明，神经网络的表现并不比Logit或线性判别估计器好很多。鉴于这一结果，我们认为，更传统的方法应该被依赖，因为它们增强了检验假设的能力。
Beck, King, and Zeng (2000) offer both a sweeping critique of the quantitative security studies field and a bold new direction for future research. Despite important strengths in their work, we take issue with three aspects of their research: (1) the substance of the logit model they compare to their neural network, (2) the standards they use for assessing forecasts, and (3) the theoretical and model-building implications of the nonparametric approach represented by neural networks. We replicate and extend their analysis by estimating a more complete logit model and comparing it both to a neural network and to a linear discriminant analysis. Our work reveals that neural networks do not perform substantially better than either the logit or the linear discriminant estimators. Given this result, we argue that more traditional approaches should be relied upon due to their enhanced ability to test hypotheses.