13988 Rar Apr 2026

Residual-based Adaptive Refinement is a strategy used to improve the accuracy and efficiency of by intelligently selecting training data points.

: 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

: It is generally more memory-efficient than strategies that constantly add new points to the dataset. Weaknesses : Residual-based Adaptive Refinement is a strategy used to

: 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 : Weaknesses : : Other sophisticated adaptive strategies can

The search result for "13988 rar" primarily refers to a scientific paper on arXiv:2112.13988 , which discusses a machine learning technique called . Review of RAR in Machine Learning