This research paper focuses on the implementation of radial Basis Function (RBF) Support Vector Machines (SVM) for classifying asteroid orbits. Asteroids are important astronomical objects, and their orbits play a crucial role in understanding the dynamics of the solar system. The International Astronomical Union maintains data archives that provide a playground to experiment with various machine-learning techniques. In this study, we explore the application of RBF SVM algorithm to classify asteroids. The results show that the RBF SVM algorithm provides a good efficiency and accuracy to the dataset. We also analyze the impact of various parameters on the performance of the RBF SVM algorithm and present the optimal parameter settings. Our study highlights the importance of using machine learning techniques for classifying asteroid orbits and the effectiveness of the RBF SVM algorithm in this regard.

本研究聚焦于使用径向基函数支持向量机算法对小行星轨道进行分类，研究结果表明这种方法在数据集方面效率和准确性均较好，并给出最佳参数设置，研究强调了使用机器学习技术分类小行星轨道的重要性及该算法的有效性。

使用径向基函数在支持向量机上实现小行星的轨道分类