论文标题
关于多项式神经网络样品复杂性的研究
On the Study of Sample Complexity for Polynomial Neural Networks
论文作者
论文摘要
作为一般类型的机器学习方法，人工神经网络已在许多模式识别和数据分析任务中建立了最先进的基准。在各种神经网络体系结构中，多项式神经网络（PNN）最近已证明可以通过神经切线内核进行频谱分析来分析，并且在图像产生和面部识别方面特别有效。但是，获得对PNNS的计算和样本复杂性的理论见解仍然是一个开放的问题。在本文中，我们将以前文献中的分析扩展到PNN，并获得有关PNN样品复杂性的新结果，该结果为解释PNN的概括能力提供了一些见解。
As a general type of machine learning approach, artificial neural networks have established state-of-art benchmarks in many pattern recognition and data analysis tasks. Among various kinds of neural networks architectures, polynomial neural networks (PNNs) have been recently shown to be analyzable by spectrum analysis via neural tangent kernel, and particularly effective at image generation and face recognition. However, acquiring theoretical insight into the computation and sample complexity of PNNs remains an open problem. In this paper, we extend the analysis in previous literature to PNNs and obtain novel results on sample complexity of PNNs, which provides some insights in explaining the generalization ability of PNNs.
