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

TL;DR

This paper demonstrates that radial Basis Function Support Vector Machines effectively classify asteroid orbits with high accuracy, emphasizing the importance of machine learning in astronomical data analysis.

Contribution

It applies RBF SVM to asteroid orbit classification, analyzing parameter impacts and identifying optimal settings, which is a novel application in this context.

Findings

RBF SVM achieves high classification accuracy

Optimal parameters improve performance significantly

Machine learning enhances asteroid orbit analysis

Abstract

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.