Networks with Finite VC Dimension: Pro and Contra
The paper investigates whether having a finite VC dimension for function classes is beneficial for learning versus function approximation. While finite VC dimension ensures uniform convergence of empirical errors (good for learning), it can be detrimental to approximating functions drawn from a specific probability distribution, as approximation errors concentrate around the mean value. The outcome depends on how the growth function relates to other complexity measures.