Orbit Classification of asteroids using implementation of radial Basis
  Function on Support Vector Machines
Yashvir Tiberwal Nishchal Dwivedi
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.