Weighting Function

Weighting functions are used in machine learning to adjust the influence of individual data points during model training, addressing issues like class imbalance, noisy labels, and covariate shift. Current research focuses on developing adaptive weighting schemes that learn optimal weights from data, rather than relying on pre-defined functions, often employing neural networks or meta-learning approaches. These advancements improve model robustness and generalization, particularly in challenging scenarios with noisy or biased data, leading to more accurate and reliable predictions across various applications, including object detection, natural language processing, and regression tasks.