Approximation error of single hidden layer neural networks with fixed weights

This work addresses a theoretical problem in machine learning for researchers, but it appears incremental as it focuses on a specific case without broader application claims.

The paper tackles the problem of quantifying the approximation error for single hidden layer neural networks with two fixed weights, providing an explicit formula for this error.