To compute F (x), we need to calculate, for each boosting round m, This expression is indicative of a gradient descent step: where ρ m is the step length and To solve a m and w m , we make the basis functions f m (x i ; a, w) correlate most to −g m (x i ), where the gradients are defined over the training data distribution.
using Taylor series expansion, we can get closed form solutions for a m and w m -see Chen and Guestrin (2016) for details.