Instance Based Learning

Instance-based learning (IBL) focuses on learning from individual data points (instances) rather than abstracting to general rules, aiming to improve prediction accuracy and interpretability in various domains. Current research explores IBL's application in diverse fields, including image analysis, time-series forecasting, and knowledge base completion, often integrating it with ensemble methods, Bayesian nonparametric frameworks, or large language models to address challenges like computational complexity and data scarcity. The resulting models offer advantages in handling noisy data, providing uncertainty estimates, and improving the efficiency of resource-constrained applications such as prosthetic control and real-time localization. This approach holds significant promise for enhancing the accuracy and explainability of machine learning models across numerous scientific and engineering disciplines.