Similarity Based

Similarity-based methods are transforming various fields by leveraging the relationships between data points to improve model performance and interpretability. Current research focuses on developing novel similarity metrics and integrating them with machine learning models, including neural networks (e.g., Siamese networks, transformers) and ensemble methods, to address challenges in diverse applications such as image and text classification, reinforcement learning, and performance optimization. These advancements are leading to more accurate predictions, improved data efficiency, and enhanced model explainability across domains, impacting fields ranging from astronomy to healthcare.