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

Orbit Classification of asteroids using implementation of radial Basis Function on Support Vector Machines