Description: Overlapping models are a category within machine learning characterized by sharing some common parameters or structures among different models. This technique allows for combining the strengths of multiple approaches, facilitating generalization and improving accuracy in complex tasks. Essentially, overlapping models aim to optimize performance by leveraging the interrelationship between different models, which can result in greater robustness and efficiency. These models can be used in various applications, from natural language processing to computer vision, where the integration of different perspectives can enrich data analysis and interpretation. The flexibility of overlapping models also allows for their adaptation to a variety of domains and types of data, making them a valuable tool in the modern machine learning arsenal.
