Inverse Distance Weighting

Inverse distance weighting (IDW) is a spatial interpolation technique assigning weights inversely proportional to the distance from a data point to its neighbors, used to estimate values at unsampled locations. Current research explores IDW's application in diverse fields, including improving word embeddings in natural language processing, enhancing federated learning algorithms for contextual bandits and time series classification, and refining performance prediction in machine learning. These advancements demonstrate IDW's versatility as a foundational method for improving the accuracy and efficiency of various machine learning models and algorithms across different data types and applications.