Research Article
Optimizing Computer Worm Detection Using Ensembles
Algorithm 5
Gradient boosting.
Inputs: | |
(i) input data (x, y) Ni=1 | |
(ii) number of iterations M | |
(iii) choice of the loss-function (y, f) | |
(iv) choice of the base-learner model h (x, θ) | |
Algorithm: | |
(1) initialize f0 with a constant | |
(2) for t = 1 to M do | |
(3) compute the negative gradient gt(x) | |
(4) fit a new base-learner function h(x, θt) | |
(5) find the best gradient descent step-size ρt : ρt = arg | |
min ρ N i=1 yi, ft−1(xi) + ρh(xi, θt) | |
(6) update the function estimate: ft ← ft−1 + ρth(x, θt) | |
(7) end for |