The scientists developed a new method to improve support vector regression models when dealing with outliers and high leverage points in data. They combined different types of SVR models and kernel functions to create a more robust algorithm. By using a double SVR technique, they were able to detect and reduce the impact of unusual data points, leading to better predictions. The effectiveness of the new technique was confirmed through simulations and analysis of real-world data sets.