论文标题
基于卷积神经网络的图像分类器的收敛速率
On the rate of convergence of image classifiers based on convolutional neural networks
论文作者
论文摘要
定义了基于卷积神经网络的图像分类器，并分析了估计值对最佳错误分类风险的分类风险的收敛速率。在适当的假设对aposteriori概率的平滑度和结构的假设下，表明收敛速率与图像的维度无关。这证明在图像分类中，可以通过卷积神经网络规避维度的诅咒。
Image classifiers based on convolutional neural networks are defined, and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk is analyzed. Under suitable assumptions on the smoothness and structure of the aposteriori probability a rate of convergence is shown which is independent of the dimension of the image. This proves that in image classification it is possible to circumvent the curse of dimensionality by convolutional neural networks.
