: It significantly improves the speed at which a model converges to a solution.
Residual-based Adaptive Refinement is a strategy used to improve the accuracy and efficiency of by intelligently selecting training data points. 13988 rar
: Other sophisticated adaptive strategies can become computationally expensive as the number of training points accumulates over time. RAR is often viewed as a more balanced fit because it can refine the model without letting the training set grow uncontrollably. Strengths : : It significantly improves the speed at which
: Traditional RAR does not differentiate between points if they all have "large" residuals, which can lead to less optimal point selection compared to more modern active-learning-based ranking methods. arXiv:2112.13988v1 [math.NA] 28 Dec 2021 13988 rar