Induction and polynomial networks
Induction and polynomial networks
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DOI：
10.1109/icsmc.1995.537877
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发表时间：
1995-10
期刊：
影响因子：
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通讯作者：
J. Elder;Donald E. Brown
中科院分区：
文献类型：
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作者：
J. Elder;Donald E. Brown
归纳法在各种各样的应用领域中起着重要的作用。由于这种广泛的适用性，已经提出了各种方法，并采用从数据中发现一般模型。这些方法的一个关键目标是在模型构建过程中看不到的数据上表现良好。本文调查了各种技术可用于归纳和分类，他们的自动化程度。然后，作者更详细地研究了多项式网络，这是从控制论和早期神经网络研究中发展出来的归纳方法。作者在论文的最后提出了在多项式网络中继续工作的建议方向。
Induction plays a major role in a wide variety of application domains. Because of this broad range of applicability a variety of approaches have been suggested and employed to discover general models from data. A key goal in these approaches is to perform well on data not seen during the model construction process. This paper surveys the variety of techniques available for induction and categorizes them by their degree of automation. The authors then examine in more detail polynomial networks which are induction methods that grew out of cybernetics and early neural network research. The authors conclude the paper with suggested directions for continued work in polynomial networks.