Image classification is considered, and a hierarchical max-pooling model with additional local pooling is introduced. Here the additional local pooling enables the hierachical model to combine parts of the image which have a variable relative distance towards each other. Various convolutional neural network image classifiers are introduced and compared in view of their rate of convergence. The finite sample size performance of the estimates is analyzed by applying them to simulated and real data.

翻译：考虑对图像进行分类,并引入一个等级最大集合模型,加上额外的本地集合。在此,额外的本地集合使高空模型能够将图像中彼此相对距离不一的部分结合起来。引入了各种进化神经网络图像分类器,并根据其趋同率进行了比较。通过将这些模型应用于模拟和实际数据,对估计数的有限样本大小性能进行了分析。