Andrew Polar and Mike Poluektov

### $$y_i = \sum_{j=1}^m f_j (x_{ij}), \quad _{i=1,2,...}$$

are usually compared to linear regression

### $$y_i = \sum_{j=1}^m h_j \cdot x_{ij} = \mathbf{h^T x_i}, \quad _{i=1,2,...}$$

Usual approach to identification of the model functions $f_j$ is function-by-function descent, where approximation to each function is improved one-by-one, having all others known