Generalization Analysis for Classification on Korobov Space

The challenge for misclassification problem in practice is that as the dimension grows large, the feature becomes into special forms. Therefore, a special structure contribution is required. The performance of classification of functions from Korobov space using shallow networks might be one of the possibilities to deal with the well. A binary classification problem with an input (compact metric) space X of instances and output space Y = { 1, 1} of two labels aims at learning a (binary) classifier from samples that separate the instances in X into two classes.