Offline and online SVM performance analysis

Abstract

To understand and evaluate the performance of a machine learning algorithm, the Support Vector Machine, this thesis compares the strengths and weaknesses between the offline and online SVM. The work includes the performance comparisons of SVMLight and LaSVM, with results of training time, number of support vectors, kernel evaluations, and test accuracies. Multiple datasets are experimented to cover a wide range of input data and training problems. Overall, the online LaSVM has trained with less time and returned comparable test accuracies than SVMLight. A general breakdown of the two algorithms and their computation efforts are included for detailed analysis.