Black-box learning algorithm using error prediction levels
This is a very simple black-box learning algorithm which uses higher order error prediction to improve speed and accuracy of search to find local minima.
See paper about Error Predictive Learning
In error predictive learning, extra terms are added to the error function such that the search algorithm must learn to predict error, error in predicted error, and so on. This information is used in a non-linear way to adapt search behavior, which in turn affects error prediction etc.
This algorithm is useful for numerical function approximation of few variables due to high accuracy.
In black-box learning, there are no assumptions about the function. This makes it hard to use domain specific optimizations such as Newton's method. The learning algorithm need to build up momentum in other ways.
Counter-intuitively, forgetting the momentum from time to time and rebuilding it might improve the search. This is possible because re-learning momentum at a local point is relatively cheap. The learning algorithm can takes advantage of local specific knowledge, to gain the losses from forgetting the momentum.
Stores fit data.
Stores training settings.
Trains to fit a vector of weights on a black-box function returning error.